Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications ll Research article Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors uncovers metabolites that modulate defense against pathogens Antoni Garcia-Molina1,* and Victoria Pastor2 1 Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, C/Vall Moronta, Edifici CRAG, 08193 Bellaterra (Cerdanyola del Vallès), Barcelona, Spain 2 Department of Biology, Biochemistry, and Natural Sciences, School of Technology and Experimental Sciences, Universitat Jaume I, 12006 Castelló de la Plana, Spain *Correspondence: Antoni Garcia-Molina (antoni.garcia@cragenomica.es) https://doi.org/10.1016/j.xplc.2023.100645 ABSTRACT Understanding plant immune responses is complex because of the high interdependence among biological processes in homeostatic networks. Hence, the integration of environmental cues causes network rewiring that interferes with defense responses. Similarly, plants retain molecular signatures configured under abiotic stress periods to rapidly respond to recurrent stress, and these can alter immunity. Metabolome changes imposed by abiotic stressors are persistent, although their impact on defense remains to be clar- ified. In this study, we profiled metabolomes of Arabidopsis plants under several abiotic stress treatments applied individually or simultaneously to capture temporal trajectories in metabolite composition during adverse conditions and recovery. Further systemic analysis was performed to address the relevance of me- tabolome changes and extract central features to be tested in planta. Our results demonstrate irreversibility in major fractions of metabolome changes as a general pattern in response to abiotic stress periods. Func- tional analysis of metabolomes and co-abundance networks points to convergence in the reconfiguration of organic acid and secondary metabolite metabolism. Arabidopsis mutant lines for components related to these metabolic pathways showed altered defense capacities against different pathogens. Collectively, our data suggest that sustained metabolome changes configured in adverse environments can act as mod- ulators of immune responses and provide evidence for a new layer of regulation in plant defense. Key words: abiotic stress, Arabidopsis, biotic stress, combinatorial stress, metabolomics, systems biology Garcia-Molina A. and Pastor V. (2023). Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors uncovers metabolites that modulate defense against pathogens. Plant Comm. 4, 100645. INTRODUCTION moreover, occupy central positions as hubs (Mukhtar et al., 2011; Weßling et al., 2014). Because of the high connectivity of Plant immunity relies on sophisticated interplays among multiple immune features, network reconfigurations that activate and diverse features that act coordinately to counteract path- signaling pathways, fine-tune transcript and protein expression ogen progression (Hillmer et al., 2017; Delplace et al., 2022; levels, or adjust metabolite abundance to mediate acclimation Ngou et al., 2022; Jia et al., 2023). Defense mechanisms are to environmental perturbations can ultimately interfere with well characterized in isolation; however, pathogen infections plants’ capacity to cope with pathogen infection (Saijo and frequently occur in combination with other abiotic stressors, a Loo, 2020; Zarattini et al., 2021). In the model plant threat aggravated by climate change, producing responses Arabidopsis thaliana (hereafter Arabidopsis), defense that differ from those evoked by individual stressors (Atkinson et al., 2013; Prasch and Sonnewald, 2013; Suzuki et al., 2014; Son and Park, 2022). Systemically, components that mediate Published by the Plant Communications Shanghai Editorial Office in defense are entangled with the signaling and regulatory association with Cell Press, an imprint of Elsevier Inc., on behalf of CSPB and networks that operate to balance plant homeostasis and, CEMPS, CAS. Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 1 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens Figure 1. Setup for sequential stress treat- ments. Arabidopsis plants were cultivated under 12 h/12 h day–night cycles (22 C at 120 mmol m 2 s 1 and 18 C in the dark) (green arrows) and treated with moderate abiotic stress conditions consisting of high light (HL), humidity, drought, heat, cold, or their combinations (orange arrows) as described in the section ‘‘Methods’’ before Pseudomonas syringae (Psto) or Botrytis cinerea (Bot) challenge (yellow arrows). For the recovery design, 12-day-old Ara- bidopsis seedlings exposed to abiotic stressors for 3 days were returned to standard conditions for three additional days before pathogen challenge. For the endpoint design, the abiotic stressors were applied to 15-day-old Arabidopsis plantlets for 3 days immediately before pathogen challenge. Plants not exposed to abiotic stressors were used to evaluate the innate immune response (control). Harvest time points for molecular analysis are indicated. responses to Pseudomonas syringae (hereafter Psto) are ory’’ has motivated a large number of studies in the field of plant hindered by elevated temperatures (Wang et al., 2009) but stress (Hannan Parker et al., 2022; Charng et al., 2023; Wilkinson enhanced under water shortage (Gupta et al., 2016). Thus, the et al., 2023). Transcriptional memory consists of epigenetic outcome of plant–pathogen interactions depends largely on modifications at promoters of stress-related genes in the form the complex interaction between defense mechanisms and of histone modifications and changes in nucleosome occupancy responses to environmental cues. after stress periods that increase chromatin accessibility (Charng et al., 2023). Among other strategies for the persistent activation Combinatorial stress refers to the application of multiple of stress defense mechanisms, it is well known that, after stressors not only simultaneously but also sequentially. For exposure to elevated temperatures, plants accumulate instance, drought episodes, temperature shifts, or pathogen chaperones that assist in protein refolding to prevent recurrent infections occur in transient and uncoordinated waves. In this disruptions in protein homeostasis (Yeh et al., 2012; Fernández- case, responses to intermittent stressors do not rely on recurrent Bautista et al., 2018). Because abiotic stress conditions transitions from basal to acclimation states owing to high energy generally cause cellular damage due to oxidative bursts, plants costs that potentially trade off with plant fitness (Heil, 2002; van frequently retain the battery of antioxidant enzymes active upon Hulten et al., 2006). Instead, plants retain physiological and stress removal (Foyer et al., 2009; Juszczak et al., 2016). molecular signatures configured under unfavorable conditions Another strategy found in plants that have recovered from that allow rapid and enhanced responses in the case of conditions that cause cellular disruption, such as cold, drought, subsequent stress events, a process termed ‘‘priming’’ or or salinity, is the accumulation of osmoprotectant solutes such ‘‘stress memory’’ (Charng et al., 2023). However, the primed as mannitol, raffinose, or galactinol (Taji et al., 2002). state of plants after abiotic stress periods may hamper immune responses, enhancing susceptibility to subsequent pathogen The necessity of deciphering mechanisms of stress memory has infection or, conversely, boost defense and confer cross- recently motivated increasing interest in monitoring the recovery tolerance. As an example, adult Arabidopsis that had recovered dynamics of the physiological and molecular state of plants after from heat shock displayed increased susceptibility to Psto adverse conditions. System-wide analyses of de-acclimated plants (Janda et al., 2019), whereas cold pre-treatments led to cross- under moderate stress conditions have consistently shown revers- tolerance (Griebel et al., 2022). Therefore, deciphering the ibility of transcriptome changes, whereas metabolomes tend to be molecular mechanisms underlying stress memory is of utmost steady over time (Kaplan et al., 2004; Zuther et al., 2015; Coolen importance for identifying features that mediate the cross-talk et al., 2016; Pagter et al., 2017; Vyse et al., 2019; Garcia-Molina between defense and acclimation to abiotic stressors and under- et al., 2020). Plant metabolic state can be interpreted as the standing new layers of complexity in immune responses. consequence of the integration of the functionality of all biological processes, and metabolites can therefore act as efficient In general terms, stress memory consists of either the sustained homeostatic signals. However, the potential role of metabolites in activation of responsive components after stressor removal or mediating cross-tolerance has mostly been overlooked. To fill this the acquired capacity to escalate stress responses in the case gap, we profiled the dynamics of transcriptome and metabolome of a second event. However, the operating signatures that confer changes in Arabidopsis under a wide array of single and combined plant tolerance to stress recurrence remain far from completely abiotic stressors: high light intensity, humidity, water availability, understood. For priming, the concept of ‘‘transcriptional mem- and temperature fluctuations. Under our conditions, metabolome 2 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications Figure 2. Overview of plant immune phenotypes under sequential abiotic–biotic stress. Binary map summarizing immune phenotypes of Arabidopsis plantlets upon Psto (Pseudomonas) and Bot (Botrytis) infection in the recovery and endpoint designs in Supplemental Figure 2. Color code depicts the average colony titer (Psto) or fungal biomass (Botrytis). Fold change (FC) was calculated in comparison to standard conditions (standard). Asterisks indicate significant differences relative to control (standard) conditions (adjusted P % 0.05, Duncan’s post hoc test on linear mixed models, n = 3 independent biological experiments). changes tended to be irreversible, whereas transcriptional re- To evaluate Arabidopsis immune responses after abiotic stress sponses returned to the basal state. Further integrative analysis re- periods in a comprehensive manner, two sequential stress setups vealed metabolites involved in pathways for carbon metabolism were designed. First, 12-day-old seedlings cultivated under stan- and other pigments as common elements in the sustained dard conditions were exposed to abiotic stressors for 3 days and response, and pathogen bioassays with mutant lines proved rele- then recovered for an additional 3 days under standard condi- vant for defense responses in planta. Thus, our data contribute to- tions to establish a lag phase before pathogen infection (recovery ward generalizing the durability of metabolome reconfigurations setup, Figure 1 and Supplemental Figure 1). Second, another during recovery from diverse single and multiple abiotic stress fac- batch of plants was treated with abiotic stressors at day 15 and tors and propose central metabolites accumulated during acclima- subsequently infected with pathogens (endpoint setup, Figure 1 tion as modulators of immune responses to a wide variety of path- and Supplemental Figure 1). ogens. These observations are fundamental to establishing biotechnological strategies to reinforce plant tolerance under real The outcome of the pathogen bioassays was quantified as Psto titer scenarios. or Bot fungal biomass in leaves at 3 days post infection (dpi) (Figure 2 and Supplemental Figure 2). Plants in the recovery setup showed RESULTS opposite trends in immune phenotype depending on the pathogen. Plants recovered from abiotic stressors showed an Arabidopsis immune responses after sequences of overall enhanced susceptibility to Psto: bacterial titers were abiotic–biotic stress depend on the lag phase between approximately 1.6 to 7.5 times higher compared with controls, stressors and pathogen depending on the treatment. Conversely, a general increase in Bot Under sequential stress conditions, physiological and molecular tolerance was observed: fungal biomass in leaves was reconfigurations of plants in response to a first stressor can approximately 35%–60% lower than under standard conditions confer increased susceptibility or tolerance in advance of a sec- (Figure 2 and Supplemental Figure 2). However, the same abiotic ond stress event. In this regard, we asked how Arabidopsis im- stressors applied in the endpoint setup produced similar immune mune responses were modulated by moderate environmental responses to both Psto and Bot infection. HL-, high-humidity-, fluctuations in light, humidity, and temperature. High-light (HL) cold-, and cold-HL-treated plants accumulated approximately 2– treatments consisted of a 2.5-fold increase in light intensity rela- 16 times higher Psto titers and approximately 60%–300% more tive to standard conditions (300 vs. 120 mmol photons m 2 s 1); Bot fungal biomass compared with those under standard conditions, high humidity was achieved by watering plants in excess in whereas bacterial titers under drought and heat and fungal biomass sealed trays; and drought periods were imposed by withholding under heat and heat-drought were both halved (Figure 2 and water. For heat conditions, the diurnal and nocturnal tempera- Supplemental Figure 2). Collectively, these observations support tures were increased by 5 C (27 C day/23 C night), whereas for the existence of molecular footprints after abiotic stressors that cold, they were lowered by 10 C (12 C day/8 C night) and the affect general features involved in immune defense networks. diurnal light intensity was set to 80 mmol photons m 2 s 1. Com- binations of heat and drought (heat-drought), heat and HL (heat- HL), heat, drought, and HL (heat-drought-HL), and cold and HL Abiotic stressors produce reversible plant (cold-HL) were also considered in order to evaluate the effects transcriptome responses but persistent metabolome of multiple simultaneous stressors. Pathogen infections were reconfigurations performed using two pathogens with distinct lifestyles: the hemi- Because our results demonstrated that Arabidopsis immune re- biotrophic bacterium Psto and the necrotrophic fungus Botrytis sponses can be modulated by previous exposure to abiotic cinerea (hereafter Bot). stressors, we asked which molecular features might serve as Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 3 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens Figure 3. Temporal fluctuations in metabolite abundance under abiotic stress periods. Untargeted LC–MS/MS was used to profile metabolite abundance in plants exposed to multiple abiotic stress conditions in the recovery and endpoint setups. SAMs detected in each abiotic stress treatment relative to control conditions (adjusted P % 0.05, Dunnett’s test on linear mixed models, n = 5 independent biological experiments) were used to draw heatmaps with hierarchical clustering according to the WardD2 method. Metabolite abundances were Z-score normalized to visualize temporal patterns in metabolome changes from days 11–18 in plants cultivated under standard conditions (standard) and in the recovery and endpoint setup designs. Horizontal breaks in the heatmaps represent groups of metabolites that displayed similar patterns according to unsupervised k-means clustering. The directions of the arrows and the blue bars indicate the order of time points (11–18 days) and the duration of abiotic stress treatments, respectively. signatures for stress memory. Metabolites attracted our interest were defined as significantly altered metabolites (SAMs) (adjusted because previous systemic studies in plants recovered from unfa- P % 0.05, Dunnett’s post hoc test on linear mixed models) vorable environments revealed the reversibility of transcriptome (Supplemental Table 1). Accordingly, approximately 400 SAMs changes but the durability of metabolome reconfigurations were identified under heat and heat-HL; approximately 500 under (Kaplan et al., 2004; Zuther et al., 2015; Coolen et al., 2016; HL, drought, and heat-drought-HL; and approximately 600 under Pagter et al., 2017; Vyse et al., 2019; Garcia-Molina et al., 2020). humidity, heat-drought, cold, and cold-HL (Supplemental Thus, to assess the dynamics of metabolome responses to Figure 3). Z means of SAM abundances detected for each abiotic abiotic stressors under our conditions, the metabolite stressor were used to draw heatmaps with hierarchical clustering composition of Arabidopsis rosettes was monitored daily by to compare the dynamics of stress-responsive metabolites under untargeted liquid chromatography–tandem mass spectrometry standard conditions and both recovery and endpoint time courses. (LC–MS/MS) metabolomics. Stringent deconvolution of Unsupervised k-means heatmap partitioning was used to extract chromatograms resulted in a coverage of 10 414 potential sets of compounds that displayed similar temporal fluctuations metabolites; those that displayed differential accumulation over (Figure 3). Overall, SAMs either remained steady under standard time in each time course compared with standard conditions conditions or changed in abundance toward the end of the time 4 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications Figure 4. Transcriptome changes after abiotic stress periods. RNA-seq analysis was performed on Arabidopsis plantlets on day 18 of the recovery and endpoint setups. (A) Identification of patterns in transcriptome changes. Heatmap with hierarchical clustering according to the WardD2 method constructed from z means of normalized counts of samples under standard conditions and abiotic stressors in the recovery and endpoint setups. Normalized counts were filtered (P % 0.05, one-way ANOVA) to include genes that changed in at least one treatment. (B) Fraction of differentially expressed genes (DEGs). DEGs compared with standard conditions (FC R 2 and adjusted P % 0.05, Wald test, n = 3 in- dependent experiments) were only detected in the endpoint setup. Bar plot depicts the total numbers of DEGs under each stressor. course, likely owing to developmental and/or growth events endpoint setups, were centered on the highest positive values (r = (Figure 3). By contrast, SAM abundances shifted from the 0.8–1.0) with almost the same density, indicating that equal fractions moment of exposure to abiotic stressors in both recovery and of reversible and irreversible metabolome changes took place under endpoint setups (from day 13 or 16, respectively) (Figure 3). these conditions (Supplemental Figure 4, red vs. blue lines). SAM Interestingly, the trends in SAM composition changes in response abundances in heat-, heat-HL-, heat-drought-HL- and cold-HL- to stress treatments looked identical in both time courses recovered plantlets were poorly correlated with those under stan- (Figure 3). Next, correlations between SAM abundances in pre- dard conditions, as PCCs were more broadly distributed. However, treated (days 11 and 12) and stress-treated plants in the recovery PCC distribution was more skewed toward the highest positive setup (days 13–15) and the corresponding time points in the values in comparison with endpoint setups (Supplemental endpoint setup (days 11 and 12 and 16–18) were computed for Figure 4, red vs. blue lines), meaning that metabolic changes each treatment to quantify the similarity of metabolome reconfigu- under heat, heat-HL, heat-drought-HL, and cold-HL treatments rations in both cases. Density plots in Supplemental Figure 4 were predominantly irreversible. Finally, PCC distributions for (orange lines) depicted distributions of Pearson correlation drought treatments revealed important dissimilarities in reconfigura- coefficients (PCCs, r) centered on the highest positive values (r = tion of metabolome dynamics in recovery from water shortage 0.8–1.0) in all treatments, except for heat-drought-HL (r = 0.5–0.8). compared with both standard conditions and endpoint setups These data indicated that SAMs qualified as changing compounds (Supplemental Figure 4, red and blue lines). Consequently, our strictly because of stress responsiveness and, moreover, demon- data demonstrate that important fractions of metabolome strated that our treatments provoked almost identical metabolome changes in response to multiple abiotic stress treatments were reconfigurations in plants during intermediate developmental irreversible under our conditions. stages. Next, we were interested in determining whether the dynamics of Heatmaps also revealed that metabolome changes during stress transcriptional responses to abiotic stress followed similar pat- exposure could either return to the initial state or persist upon stress terns. To that end, normalized counts from RNA sequencing removal in the recovery setup, the fraction of metabolites that fol- (RNA-seq) of Arabidopsis rosettes at day 18 (Supplemental lowed each pattern being dependent on the treatment (Figure 3). Table 2) were used to draw a heatmap with hierarchical Thus, to estimate the degree of reversibility of metabolome clustering to visualize the transcriptome under each stressor at responses to stress treatments, SAM abundances in pre-treated the end of the time courses. Transcriptome profiles of plants and recovered plantlets (days 11–12 and 16–18, recovery setup) after the recovery design did not exhibit important divergences were compared to those at the corresponding time points under compared to standard conditions, whereas the same treatments control conditions. In addition, comparisons to the same time points applied after the endpoint setup revealed important differences, in the endpoint setup were considered in order to evaluate irrevers- mainly due to increases in transcript levels under drought, heat- ibility, given the conservation of metabolome reconfigurations when drought, heat-drought-HL, cold, and cold-HL conditions stressors were applied in both time courses. PCC distributions for (Figure 4A). To quantitatively assess the effects of stressors on metabolomes of HL, high humidity, heat-drought, and cold- transcriptomes, differentially expressed genes (DEGs) were recovered plantlets and those under control conditions, as well as identified based on an absolute fold change (FC) R2 in Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 5 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens transcript levels relative to standard conditions (adjusted P % metabolome changes, SAMs were first annotated based on the 0.05, Wald test). Fewer than 10 DEGs were detected at the end Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCyc, of the recovery phase, except under drought conditions and internal libraries (see section ‘‘methods’’). Despite technical (approximately 500 DEGs) (Supplemental Table 3), and further limitations to the proper identification of metabolites in our transcriptome analysis failed to detect significant differences in platform, this approach identified 44.07% of all SAMs as alternative splicing events relative to standard conditions (see potential markers and annotated 27.16% of them (Supplemental section ‘‘methods’’). However, the endpoint design led to Table 4). Significantly enriched KEGG pathways were identified remarkable changes in transcript abundance, although the (adjusted P % 0.05, Fisher’s exact test), and the resulting number of DEGs depended on the treatment. The lowest DEG metabolic pathways were grouped into major categories fractions were detected after HL, heat, heat-HL (fewer than 60 (carbon, nitrogen, nitrogen base, lipid metabolism, etc.) DEGs) (Figure 4B; Supplemental Table 3). Greater changes took (Figure 5). Biosynthesis of secondary metabolites was the place after high humidity (approximately 250 DEGs), cold, and category that included the highest number of tentatively heat-drought-HL (approximately 1000 DEGs), whereas the high- annotated metabolites, and those related to flavonoids and est fractions were found after drought, cold-HL, and heat- anthocyanins were commonly enriched under all abiotic stress drought (approximately 5000, 6600, and 8000 DEGs) (Figure 4B; treatments (Figure 5). Abiotic stressors generally altered carbon Supplemental Table 3). Apart from the rapid reversibility of metabolism, because the tricarboxylic acid (TCA) cycle was potential early transcriptome events not covered by our enriched under all treatments except HL and cold-HL and the strategy, we attributed the differences in DEG fractions among metabolism of ascorbate, 2-oxocarboxylic acids, and glyoxylate treatments to stress intensity, suggesting that a two-fold was enriched under multiple abiotic stressors (Figure 5). increase in light intensity or an overall temperature increase of Significant enrichment of pathways involved in the metabolism 5 C, as well as high humidity, were the mildest stressors in our of several amino acids was found only under humidity, heat, designs. Furthermore, the disparity in the range of alterations in heat-drought, and heat-HL and showed no consistent pattern, metabolites and transcripts by the end of the endpoint setup suggesting that each stressor provoked a specific reconfiguration (approximately 400–600 SAMs vs. approximately 60–8000 of nitrogen metabolism (Figure 5). Other central pathways, such DEGs) indicates additional layers of regulation for metabolic as the metabolism of nitrogen bases, fatty acids, hormones, or pathways, especially under less adverse conditions. glucosinolates, were enriched under a few treatments, and these metabolic perturbations might therefore be condition Collectively, our high-throughput analyses confirm that molecular specific (Figure 5). Together, these results suggest that mechanisms of acclimation to transient exposure to abiotic treat- alterations in secondary metabolites and TCA intermediates are ments consist of a reversible reconfiguration of the transcriptome mainly used by plants as a general response to cope with but durable changes in the metabolome after stress removal. detrimental periods, although specific aspects of metabolome reconfiguration were also found, depending on the nature of the stressor(s). Abiotic stress periods mainly affect carbon metabolism and secondary metabolites independently of late Despite the reversibility of transcriptional responses to abiotic transcriptome responses stressors, we questioned whether the alterations observed in In our attempt to identify metabolites that carry stress memory, we metabolic pathways could potentially have been dictated by wondered whether acclimation to various abiotic stress treat- transcriptomes. Given the high similarity of metabolome recon- ments could alter common metabolic pathways. Therefore, multi- figurations in response to abiotic stressors, independent of the ple comparisons among SAMs were performed to investigate developmental stage of plants under our time courses commonalities in responses to treatments. As shown in (Figure 3 and Supplemental Figure 4), DEGs detected in each Supplemental Figure 3, approximately 45% of the humidity treatment under the endpoint setup were selected as SAMs were condition specific, whereas the specific representative for, at least, late transcriptome responsive percentages of SAMs under HL, drought, heat-drought, heat- events. Thus, significantly enriched Gene Ontology (GO) terms drought-HL, cold, and cold-HL were less than 20%, and those un- and KEGG pathways (adjusted P % 0.05, Fisher’s exact test) der heat and heat-HL were less than 7%. On the other hand, larger were identified in the DEGs to assess the impact of abiotic numbers of SAMs were found at the intersection between two stressors on biological processes and metabolic reactions, conditions for cold and cold-HL (186 SAMs), HL and cold-HL respectively, at the transcriptome level (Supplemental Tables 5 (122 SAMs), and heat-drought and heat-drought-HL (72 SAMs). and 6). Enriched GO terms supported the activation of general One hundred and forty and 40 SAMs were found at the intersec- responses to abiotic stressors (light intensity, salt, water tions of three treatments for HL, cold, and cold-HL and HL, deprivation, cold, heat) and only included terms for biotic heat-drought, and heat-drought-HL, respectively (Supplemental stressors (fungus, bacteria) under high humidity (Supplemental Figure 3). Twenty-four SAMs were common to five treatments Table 5). Indeed, steady-state levels of representative abiotic (all except heat, drought, heat-drought, heat-HL, heat-drought- stress markers were consistent with the treatments we applied, HL), 29 SAMs to six (all except HL, humidity, and cold-HL), 44 whereas selected immune-responsive transcripts did not SAMs to seven (all except HL and cold-HL), 10 SAMs to all except display remarkable induction (Supplemental Figures 5 and 6). HL, and only five to all treatments (Supplemental Figure 3). Other significantly enriched GO terms were related to Because more than 300 metabolites responded differentially hormones (auxins, cytokinins, jasmonic acid, abscisic acid, under at least three abiotic stressors compared with standard gibberellins) and the reconfiguration of central biological conditions, commonalities in metabolic responses to abiotic processes such as photosynthesis, mitochondrial function, cell stressors could be expected. Thus, to functionally characterize division, ubiquitination, or vesicle sorting, although this 6 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications Figure 5. Functional relevance of metabolome changes during abiotic stress conditions. Significant enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was determined based on tentative annotations of SAMs under each abiotic stress treatment (adjusted P % 0.05, Fisher’s exact test). Circle size is proportional to the number of metabolites included in the pathway, and color code indicates the degree of enrichment significance based on adjusted P as log10(FDR). KEGG pathways significantly enriched in transcriptome data (adjusted P % 0.05, Fisher’s exact test, Supplemental Table 6) for late-changing events are circled in blue. Pink squares highlight groups of pathways involved in the metabolism of carbon, amino acids, and secondary metabolites. depended on treatment (Supplemental Table 5). KEGG insight into systemic responses to treatments. To this end, con- pathways also reflected changes in fundamental biological ditional networks were constructed based on correlations processes and, moreover, retrieved metabolic reactions among temporal fluctuations in changing compounds (see sec- similar to those found for metabolome changes, especially tion ‘‘methods’’) (Supplemental Figure 7A). Topologically, under drought, heat-drought, and cold-HL (Figure 5; metabolome networks were similar in size (approximately Supplemental Table 6). Transcriptional changes in carbon 1500–1800 nodes/metabolites) but differed in shape and metabolic reactions under heat-drought and in secondary meta- community composition (5700–10 700 edges and 9–12 bolism under high humidity, heat-drought-HL, and cold-HL modules, depending on the condition) (Figure 6A). Because coincided with SAM enrichments under the same treatments. network topology is determined by the connectivity of However, transcriptional changes related to either the meta- metabolites according to co-abundance patterns, we assumed bolism of carbon, amino acids, and nitrogen bases under that conserved responses to treatments would lead to sets of drought and cold-HL or of amino acids, lipids, and glucosino- metabolites (nodes) with similar degrees (numbers of edges lates under heat-drought were not observed at the metabolome per node) in the networks, whereas functional rewiring would level (Figure 5). Thus, the overall inconsistency between cause changes in the numbers of node connections. Thus, metabolic reactions enriched in transcriptomes and (dis)similarities in metabolome response to abiotic stressors metabolomes tempted us to speculate that metabolome were estimated based on equivalences in node degree distribu- reconfiguration upon exposure to abiotic stressors may not tion among networks. As expected, node connectivity in the directly reflect late changes in transcriptional abundance or, standard conditions network differed markedly from that in alternatively, might be generally mediated by early-responsive the abiotic stressor networks (r < 0.63) owing to metabolome transcripts that rapidly recover. reconfigurations during treatments (Figure 6B). Metabolite connectivity in the high-humidity network displayed a low correlation (r < 0.65) with all other treatments except drought Network analysis of metabolome changes during (r = 0.68), suggesting a unique metabolome response to abiotic stress periods reveals convergent elevated humidity in plants (Figure 6B). The network reconfiguration of metabolic pathways mediated by topologies of the remaining treatments presented more different central components similarities (Figure 6B). Interestingly, the topology of the HL The unique aspects of metabolome rewiring after multiple and network was exclusively comparable to those of other moderate abiotic stressors in terms of irreversibility and partial stressors applied simultaneously with HL, i.e., heat-HL, heat-- independence from transcriptomes motivated us to gain more drought-HL, and cold-HL (r = 0.67–0.74), pointing to partial Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 7 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens Figure 6. Reconfiguration of metabolomes in Arabidopsis plants in different abiotic stress periods. Compounds that changed in at least one treatment (P % 0.05, linear mixed model, n = 5 independent experiments) were used to construct conditional networks (Supplemental Figure 7A). (A) Network topological metrics in terms of size (number of nodes/metabolites), shape (number of edges), and detected communities (>20 nodes/me- tabolites) are shown. (B) Comparison of global metabolome responses among abiotic stress treatments. Pearson correlations between the normalized node degree in each network were calculated to estimate (dis)similarities in the topology of the conditional networks. (C) Commonalities in the local hub composition of networks. The three highest values for connectivity per community were considered to be local hubs and used for multiple comparisons (Supplemental Figure 7B). Bar plots depict the total number of common local hubs among treatments. conservation of the metabolic rewiring that occurred under periods, differences in local hub composition support the HL in these stressor combinations. Likewise, important similar- specialization of central features in each response. ities (r > 0.65) in topology were found among networks derived from treatments that involved changes in temperature (heat and cold), and the same held true for drought (Figure 6B). Thus, our Central features involved in metabolome rewiring under network analysis suggests that exposure to several abiotic abiotic stressors modulate plant responses to pathogen stressors may provoke partially overlapping metabolome infection reconfigurations under our conditions. Furthermore, changes To extract the most relevant components of metabolome re- in metabolic pathways operating in response to single sponses to abiotic stressors, local hubs were investigated as rep- stressors would be expected in combinatorial responses to resentatives of the functionality of communities. Among all me- stressors applied simultaneously. tabolites that qualified as hubs, our platform provided annotations for 32 of them (Supplemental Table 7). Several Networks were further investigated for community composition anthocyanidins, such as pelargonidin derivatives, coumarin, to extract local hubs (i.e., nodes with the highest degree per mod- and cyanidin, appeared as local hubs in the networks for ule). Because of their central position, local hubs can be selected multiple treatments, as did flavonoid derivatives for naringenin to assess the preservation of the most relevant features among and quercetin in specific cases (Supplemental Table 7). Other networks. For our purpose, metabolites ranked within the top common hubs at the intersection of multiple networks were three degree values were considered to be local hubs fumaric acid, indole-3-acetic acid, 3-butenylglucosinolate, and (Supplemental Table 7). As a general trend, approximately 55% the nicotinamide derivatives dimethylmaleic acid and NAD+ of all local hubs were specific to each network, approximately (Supplemental Table 7). Complementary to this strategy, 29% were common to two or three networks, and only 7% metabolome datasets were used to construct supervised were shared among half (5) or more of the networks (Figure 6C models to identify orchestrated changes in metabolite and Supplemental Figure 7B). In consequence, despite network composition related to immune phenotypes for Psto and Bot topologies revealing partial conservation of trends in (Figure 2). Decision trees indicated an interplay of sets of metabolome changes under a wide range of abiotic stressor unknown compounds with cyanidin derivatives under Psto 8 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications Figure 7. Overview of immune phenotypes of selected candidates to pathogen infection. (A) Binary map summarizing the immune phenotypes of the indicated Arabidopsis mutant lines upon challenge with Psto (Pseudomonas), Bot (Botrytis), Plectosphaerella cucumerina (Plectosphaerella), and Colletotrichum higginsianum (Colletotrichum) in Supplemental Figure 10. Color code depicts average colony titer (Psto) or fungal biomass (Botrytis, Plectosphaerella, and Colletotrichum). FC was calculated in comparison to wild-type (WT) plantlets, and asterisks indicate significant differences from the WT (adjusted P % 0.05, Duncan’s post hoc test on linear mixed models, n = 4 independent biological experiments). (B) Frequency of immune phenotypes for selected mutants: enhanced susceptibility (susc), enhanced tolerance (tol), or ambiguous (amb). (C) Pie chart depicting the fraction of mutants that displayed significant immune phenotypes to one or several pathogens. (D) The number of mutant alleles that produced immune phenotypes to Pseudomonas (Psto), Botrytis (Bot), Plaectosphaerella (Plec), and Colletotrichum (Col). infection and with pelargonidin and coumaroyl acid under Bot starch production, glucose-6-phosphate/phosphate translocator infection to counteract pathogenic infections (Supplemental 2 (gpt2), cytosolic fructose-1,6-bisphosphatase (cybf), and Figure 8; Supplemental Table 8). Consistent with KEGG L-galactose-1-phosphate phosphatase (vtc4), which is involved pathway enrichment results (Figure 5), our metabolome-wide in ascorbate biosynthesis, were also included. Besides Psto analysis confirmed the reconfiguration of intermediates in path- and Bot, bioassays were extended to include the necrotrophic ways for flavonoid biosynthesis and TCA-organic acids as the pathogen Plectosphaerella cucumerina and the hemibiotrophic most significant changes in response to abiotic stressors. pathogen Colletotrichum higginsianum (hereafter Plec and Col) to address the consequences of mutations in response to a To evaluate whether central features in metabolome responses to broader pathogen repertoire. Despite the different steady-state abiotic stressors could affect plant defense against pathogens, levels of target genes in mutant lines (Supplemental Figure 9), mutant lines targeting intermediate steps in candidate metabolic significant differences from the WT in defense against pathways were used to test plant–pathogen interactions. For pathogens (adjusted P % 0.05, Duncan’s post hoc test on pigment biosynthesis, mutants with increased (pap1-D) or defec- linear mixed models) were found for all alleles except pap1-D tive (tt4-2 and tt4-15) anthocyanin content (Borevitz et al., 2000; and tt14-15 (Figure 7A and Supplemental Figure 10). Immune Xu et al., 2020) or reduced flavonoid composition (tt15) were phenotypes were largely mutant and pathogen dependent, as selected. For TCA-organic acids, we used loss-of-function lines Psto and Plec infections tended to show enhanced for cytosolic fumarase 2 (fum2-1 and fum2-2), succinate dehy- susceptibility in comparison with the WT, whereas Bot and Col drogenase subunit 1 (sdh2.1-1 and sdh2.1-2), and pyruvate ki- showed heterogeneous patterns (Figure 7A and Supplemental nase (pyr-1 and pyr-2) and the tonoplast dicarboxylate trans- Figure 10). However, the most relevant phenotypes caused by porter (tDT-1) mutant with altered malate and fumarate mutations consisted of enhanced susceptibility (nine cases), accumulation in leaves (Emmerlich et al., 2003). Because KEGG although ambiguous responses depending on the pathogen pathway enrichment also highlighted other carbon metabolic were found for three alleles, and only one allele (sdh2.1-2) pathways as relevant, transfer DNA (T-DNA) lines for conferred tolerance (Figure 7B). In terms of convergence, three phosphoglucose isomerase 1 (pgi1) as the central enzyme in alleles produced marked phenotypic changes under three Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 9 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens pathogens, six under two pathogens, and four under one, metabolomes is limited. Nevertheless, previous studies on causing Bot and Plec challenge in twice as many cases of plants during recovery from heat, cold, or HL reported defense alteration in mutants compared with Psto and Col sustained changes in primary metabolites (Kaplan et al., 2004; (Figures 7C and 7D). Remarkably, mutant lines that exhibited an Caldana et al., 2011; Pagter et al., 2017; Garcia-Molina et al., immune phenotype to at least two pathogens showed 2020), which could be envisaged as a signature to maintain significant differences from control lines in organic acid active stress responses. Here, non-targeted metabolomic composition and secondary metabolites, even for mutants in profiling of plants under nine abiotic stress factors applied indi- which steady-state transcript levels of target genes were vidually or simultaneously was used to obtain a broader coverage minimally altered, such as pyr-1 and pig1 (Supplemental of metabolome composition in response to a wide range of Figures 9 and 11). None of these lines displayed noticeable stressors. The temporal trajectories of metabolite abundance in physiological constraints under abiotic stress treatments in our our recovery setup confirmed the irreversibility of major metabo- setup (Supplemental Figures 12 and 13). Per group, tt15 was lite fractions after exposure to stressors (Figure 3). In addition, the pigment-production mutant that induced the most metabolome changes showed similar patterns regardless of the significant immune phenotypes, showing an approximately two- timing of treatment application, whereas transcriptome fold loss or increase in fungal biomass compared with the WT responses evoked by abiotic stressor periods were completely upon Bot and Plec infection, respectively (adjusted P % 0.05, absent in recovered plants (Figures 3 and 4). Consequently, our Duncan’s post hoc test on linear mixed models) (Figure 7A). work provides important evidence for the irreversibility of The mutations selected for TCA-organic acids significantly metabolome changes as a general strategy in plant responses enhanced susceptibility to at least two pathogens, and the de- to abiotic stress, at least in the short term. fense response of fum2 mutants was significantly impaired under all four pathogens for at least one allele (Figure 7A). Interestingly, The Arabidopsis metabolome displayed remarkable plasticity in pgi1, gpt2, and cybf alleles significantly enhanced Plec response to environmental cues, even for moderate treatments susceptibility phenotypes, revealing that balanced carbon that did not markedly disturb late transcriptome changes under metabolism is a particular requisite for counteracting Plec our experimental conditions (e.g., HL, humidity, heat, and heat- infections (Figure 7A). Finally, the ascorbate deficiency of vtc4 HL; Figures 3 and 4 and Supplemental Figure 4). Systemic produced different phenotypes depending on the pathogen: comparisons between networks of co-abundant metabolites un- enhanced susceptibility to Plec (three-fold increase in fungal der each treatment revealed similarities in topology but differ- biomass vs. WT) but enhanced tolerance to Bot and Col (30%– ences in local hub composition (Figure 6). Thus, abiotic 50% less fungal biomass vs. WT) (Figure 7A). In summary, our stressors are expected to affect different metabolites with a results demonstrate that perturbations in central metabolic high hierarchy, and these perturbations might cause pathways to cope with multiple abiotic stressors significantly subsequent reconfigurations that ultimately converge on similar alter the plant’s capacity to defend against pathogens with metabolic pathways. Indeed, despite the residual overlap in different lifestyles, revealing them as crucial features that SAMs among treatments, functional enrichments of annotated modulate Arabidopsis immunity during sequential stresses. metabolites were consistent with intermediates in the TCA cycle, metabolism of 2-oxocarboxylic acids and ascorbic acid, and secondary metabolites such as flavonoids and anthocyanins DISCUSSION (Figure 5). Limitations to the bona fide identification of The extreme complexity of plant immunity networks hampers the metabolites detected by untargeted LC–MS/MS metabolomics isolation of new components with pivotal roles in modulating de- precluded a more in-depth interpretation of changes in fense responses. In this work, we addressed the configuration of metabolite composition. However, the function of local hubs stress memory states after abiotic stressors to identify new modu- can be extended to other co-abundant metabolites according lators of plant defense. Our pathogen bioassays demonstrated that to the ‘‘guilt-by-association’’ principle, as network communities plants that had recovered from single and combinatorial fluctua- were configured based on interconnectivity among metabolites tions in light intensity, humidity, water availability, and temperature with identical behavior. Interestingly, fumaric acid and showed overall greater susceptibility to Psto but cross-tolerance to flavonoid and anthocyanin derivatives such as quercetins, pelar- Bot (Figure 2). Such an antagonistic pattern can be attributed to gonidin, or cyanidin were identified as central metabolites in the differences in pathogen lifestyles, because reinforcement of network communities of several treatments (Supplemental certain aspects of plant homeostasis in terms of primary Table 7). Therefore, our results are consistent with the eventual metabolism or antioxidant barriers after the primary stressor effect of abiotic stress conditions on TCA cycle–derived organic would be beneficial for colonization by the hemibiotrophic Psto acids and pigment composition in Arabidopsis. but would constrain progression of the necrotrophic Bot. The TCA cycle operates as a versatile metabolic crossroads that Despite most attention to priming and stress memory being paid integrates energy production and cellular respiration with amino to epigenetic mechanisms (Oberkofler et al., 2021; Hannan acid metabolism, nitrogen assimilation, and biosynthesis of sec- Parker et al., 2022; Charng et al., 2023), we wanted to explore ondary metabolites, among other processes (Sweetlove et al., the potential contribution of metabolites in detail. Accumulation 2010). In Brassicaceae, fumaric acid constitutes a major of osmoprotectants and antioxidants such as raffinose, fraction of photosynthate and therefore plays a pivotal role in galactinol, myo-inositol, or ascorbic acid is a well-known strategy the partitioning and distribution of carbon skeletons (Chia et al., for extending responses to counteract recurrent stress (Taji et al., 2000; Dyson et al., 2016). As stress responses are accompanied 2002; Foyer et al., 2009; Juszczak et al., 2016). However, our by important energy demands, plant acclimation to HL and cold understanding of the impact of abiotic stress on plant conditions depends largely on a balanced fumaric acid content 10 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications (Dyson et al., 2016), although we did not observe such homeostatic state of the plant. As a result, the abundance of dependency under moderate abiotic stress treatments central metabolites could serve as a systemic signal for (Supplemental Figures 12 and 13). Furthermore, fumaric acid, as Arabidopsis to modulate immunity and adapt defense to the well as other TCA cycle–derived organic acids, has been overall state of the plant. credited with signaling capacity to induce defense responses against Psto, although at high physiological levels (Less et al., 2011; Finkemeier et al., 2013; Balmer et al., 2018). Similarly, METHODS naringenin and other anthocyanins accumulate in adult leaves of Arabidopsis plants under several stressors to promote tolerance Plant cultivation as antioxidant barriers and can also trigger defense-related Arabidopsis thaliana ecotype Col-0 was used as the WT control. Mutant lines genes (An et al., 2021; Kaur et al., 2023). However, the for PRODUCTION OF ANTHOCYANIN PIGMENT 1 (pap1-D), TRANS- contribution of such metabolites to defense against multiple PARENT TESTA 14/CHALCONE SYNTHASE (tt14-15), and TONOPLAST pathogens has not been explored in detail. DICARBOXYLATE TRANSPORTER (tDT-1) were described previously (Borevitz et al., 2000; Emmerlich et al., 2003; Xu et al., 2020). The lines Here, immune responses were investigated in plants at interme- tt4-2 (CS2111524), TRANSPARENT TESTA 15/UDP-GLUCOSE STEROL GLUCOSYLTRANSFERASE 15 (tt15; SALK_103581), FUMARASE 2 diate developmental stages to avoid advanced differentiation be- (fum2-1, SALK_025631; fum2-2, GABI_107E05), SUCCINATE DEHYDROG tween sink and source tissues and interference caused by me- ENASE 2.1 (sdh2.1-1, SALK_031100C; sdh2.1-2, SALK_127672C),PYRU- tabolites that accumulate to high levels as plants grow or signal VATE KINASE FAMILY PROTEIN (pyr-1, SALK_049481C; pyr-2, for the flowering transition. We observed immune susceptibility SALK_014468C), PHOSPHOGLUCOSE ISOMERASE 1 (pgi1, SALK_ for fum2 mutant lines upon Psto, Bot, Plec, or Col challenge, as 107903C), GLUCOSE-6-PHOSPHATE/PHOSPHATE TRANSLOCATOR 2 well as for other mutant lines with abrogated expression of en- (gpt2, GK-454H06-018837), CYTOSOLIC FRUCTOSE-1,6-BISPHOSPH zymes catalyzing the production of succinate and pyruvate and ATASE (cybf, SALK_064456C), and INOSITOL MONOPHOSPHATASE the import of malate and fumarate into vacuoles, depending on FAMILY PROTEIN (vtc4, SALK_077222C) were requested from the Notting- the pathogen (Figure 7A). In line with the apparent dependence ham Arabidopsis Stock Centre (NASC, https://arabidopsis.info/). Seeds on carbon skeletons to ensure plant defense, potential were stratified at 4 C for 2 days and sown on Jiffy-7 substrate (Jiffy Group International, Zwijndrecht, the Netherlands) under a 12-h light (22 C and constraints to accumulating normal levels of starch in the pgi1 120 mmol m 2 s 1)/12-h dark (18 C) cycle with a relative humidity of 65% mutant would also cause alterations in fumaric acid in FitoClima 1200 PLH LED cabinets (Aralab, Rio de Mouro, Portugal). Abiotic composition and, ultimately, susceptibility to the three fungi, stress treatments consisted of raising diurnal light to 350 mmol m 2 s 1 (HL), although the steady-state transcript levels of the target gene sealing trays with excess water to preserve 100% of soil moisture and high remained unaltered (Figure 7A, Supplemental Figures 9 and 11). humidity (humidity), water shortage until approximately 30% of soil moisture On the other hand, tt4-15 and tt4-2 lines with abrogated anthocy- (drought), increasing temperature cycles to 27 C/23 C (heat), lowering tem- anin content tended to display susceptibility to the four patho- perature cycles to 12 C/8 C and adjusting light intensity to 80 mmol m 2 s 1 gens, whereas tt15 and the over-accumulator pap1-D showed (cold) or 200 mmol m 2 s 1 (cold-HL), and combinations of these conditions. tolerance to Bot and Col (Figure 7A). It is notable that mutant Rosette leaves were harvested 6 h after the onset of light and flash frozen in lines that displayed the broadest immune phenotypes exhibited liquid nitrogen. altered patterns for organic acids and secondary metabolite concentrations (Supplemental Figure 11). Thus, manipulation of central metabolites whose accumulation is Pathogen infection assays naturally disrupted in response to multiple abiotic stressors com- Psto pv. tomato DC3000 colonies cultivated at 28 C for 2 days on King’s plicates plant defense responses against several pathogens with medium (Condalab, Madrid, Spain) plates supplemented with antibiotics different lifestyles. were resuspended at an optical density 600 (OD600) of 0.05 in 10 mM MgCl2 and Silwet L77 0.02% (v/v) and used to spray Arabidopsis rosettes. Organization of plant immunity networks obeys evolutionary Bacterial growth curves at 0 and 3 dpi were quantified on the basis of col- ony forming units (CFU) from serial dilutions of leaf disk extracts on selec- forces that have fixed defense strategies, as well as regulatory tion plates. mechanisms, to ensure optimal and efficient transitions from basal to defensive states based on frequently encountered path- Fungal material was grown at 25 C in darkness for 15 days. Bot and Plec ogens (Ngou et al., 2022). Metabolites as modulators of plant were cultivated on full or 1/2 potato dextrose agar medium, respectively, immunity represent a novel, biologically relevant mechanism and fungal spores were prepared on 1/4 potato dextrose broth at 5 3 that has not been addressed in depth. The resolution of large- $105 spores ml 1. Col (O’Connell et al., 2012) was cultivated on oatmeal scale plant–pathogen protein interaction maps has revealed plates, and spores were resuspended in water at 5 3$105 spores ml 1. evolutionarily conserved strategies of pathogen infection, which Fungal biomass was quantified from entire rosette leaves at 3 dpi (Psto, involve targeting proteins centrally positioned in the network Bot) or 7 dpi (Plec, Col) by real-time qPCR. Amplification reactions were (Mukhtar et al., 2011; Weßling et al., 2014). However, none of performed with approximately 10 ng of gDNA with SYBR Green I dye these protein hubs are strictly related to metabolic pathways, using specific fungus primers and the Arabidopsis ACTIN2 (ACT2) gene as a reference (Supplemental Table 9) in a Roche Light Cycler 480 meaning that pathogens do not prioritize the direct dismantling instrument (Roche, Basel, Switzerland). The two-step protocol of metabolomes to colonize hosts. Moreover, metabolic consisted of an initial cycle at 95 C for 10 min and 45 cycles of 95 C for reactions that produce compounds capable of modulating 10 s and 60 C for 20 s. Fungal biomass was determined according to immune responses were not regulated at the transcriptome the 2 D(Ct fungus gene – Ct ACT2) method and normalized to the average level after multiple abiotic stress factors. Therefore, we propose value for controls in each biological replicate. Relative fungal biomass that the metabolome is the result of the interplay between was determined after outlier imputation, and average values were used multiple biological processes and thus reflects the final to calculate FC relative to control conditions. Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 11 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens Metabolome preparation and profiling Miscellaneous statistical and bioinformatic analyses Metabolites were extracted from 1–3 mg of lyophilized plant material in 1 Statistical tests and plotting were performed with the basic interface of ml of 30% methanol (v/v) supplemented with 0.01% formic acid (v/v). RStudio or implemented packages. Outliers and missing values in infec- Samples were homogenized with glass beads in a mixer mill for 3 min at tion assays and metabolome datasets were detected and imputed by 30 Hz and centrifuged at 14 000 rpm for 20 min. Supernatants were filtered the k-nearest neighbors method (k = 5) using the VIM package. Multiple through 0.22-mm regenerated cellulose (Phenomenex, Torrance, USA) comparisons of linear mixed models were computed according to Dun- and frozen at 80 C. Five microliters of metabolite extracts were injected can’s multiple range or Dunnett’s post hoc tests (adjusted P % 0.05). and separated in a reverse Kinetex C18 analytical column (2.6 mm particle Heatmaps with hierarchical clustering according to the Ward D2 method size, 50 mm 3 2.1 m, Phenomenex) using a gradient of methanol and H2O were drawn using z-means of normalized data with the pheatmap pack- supplemented with 0.01% formic acid. The flow rate was set to 0.3 ml/ age. UpSet plots were constructed with the UpSetR package. Supervised min. Samples were ionized in positive and negative ion modes for electro- decision trees were modeled with the rpart package using log10-trans- spray ionization (ESI) in a 40–1100 m/z range using an Acquity UPLC formed metabolite abundances of plants during the 3 days of stressor I-Class System interfaced to a hybrid quadrupole time-of-flight mass treatment and tags based on cutoffs of two-fold pathogen infection for spectrometer, SYNAPT G2-S high-definition tandem mass spectrometry Psto and 30% for Bot relative tostandard conditions. (MS/MS) detector (Waters, Milford, USA). Analysis of mutant lines Raw data were obtained using MassLynx 4.2 software (Waters) and con- For gene expression analysis, total RNA from 18-day-old Col-0 and verted to CDF format with the MassLynx DataBridge tool. Peak intensities mutant plants grown under standard conditions was prepared as indi- of compounds were retrieved using the xcms package integrated into RStu- cated above. RNA was retrotranscribed to cDNA using the NZY First- dio, referred to dry weight, and quantile normalized. Linear mixed models Strand cDNA Synthesis kit (NZYTech, Lisboa, Portugal). qPCR was per- were fitted to temporal fluctuations in metabolite abundances, and signifi- formed using specific primers and normalized with ACT2 as the reference cant differences relative to controls were computed using a post hoc Dun- gene (Supplemental Table 9) using the protocol described above for nett’s test (adjusted P % 0.05). Filtered metabolites were loaded into Mar- fungal biomass. Vis-Suite 2.0 software (Kaever et al., 2015) to obtain isotope corrections and identifications using the MarVis pathway interface with a 0.01 tolerance Organic acid profiling was performed by targeted LC–MS/MS metabolo- for m/z correction. Metabolite annotations and functional enrichment of mics as described in Balmer et al. (2018) with modifications. In brief, metabolic pathways (adjusted P % 0.05, Fisher’s exact test) were performed metabolites were extracted from 1–2 mg of dry tissue with 0.1% formic on combined lists of compounds identified in ESI+ and ESI modes using acid (v/v). Five microliters of the extracts were injected into a Xevo TQS KEGG (https://www.genome.jp/kegg/), BioCyc (https://biocyc.org/), and in- instrument (Waters Micromass, Manchester, UK) equipped with a T- ternal libraries described in Gamir et al. (2014). Metabolome datasets were wave device and coupled to a triple quadrupole through an Acquity deposited at MetaboLights (https://www.ebi.ac.uk/metabolights/) under UPLC HSS T3 2.1 column (1.8 mm, 100 mm) (Waters). The ESI was set accession number MTBLS7018. to negative for all compounds. All parameters for chromatography and MS/MS analysis were set as described in Balmer et al. (2018). Secondary compounds were prepared and analyzed as described Transcriptome profiling and analysis above for untargeted metabolomics. The ChromaLynx function in Total RNA was extracted from 18-day-old rosettes with the Maxwell RSC MassLynx 4.2 software (Waters) was used to identify secondary Plant RNA kit (Promega, Wisconsin, USA) according to the manufacturer’s metabolites against internal libraries of compounds based on retention instructions. RNA-seq libraries were prepared and paired-end sequenced times and fragments of selected standards. (2 3 150 bp) at Sistemas Genómicos (València, Spain) with standard Illu- mina protocols. RNA-seq datasets were analyzed on the Galaxy platform Photosynthetic measurements were performed on dark-adapted plants (Afgan et al., 2022). In brief, adaptors were removed from reads using after abiotic stress treatments with an IMAGING-PAM M-Series instru- Trimmomatic (Bolger et al., 2014), and clean reads were mapped to the ment (Walz, Effeltrich, Germany), and the maximum quantum yield of Arabidopsis reference genome (TAIR10) with STAR (Dobin et al., 2013). photosystem II (PSII) (Fv/Fm) was calculated as (Fm F0)/Fm). Read counts were computed with featureCounts (Liao et al., 2014), and DEGs were identified for pair-wise comparisons between standard DATA AVAILABILITY conditions and each treatment under the recovery and endpoint setup Transcriptome and metabolome data are available at GEO and (absolute log2 FC R1, adjusted P % 0.05, Wald test) using DESeq2 MetaboLights with accession numbers GSE226105 and MTBLS7018, (Love et al., 2014). Differences in alternative splicing were evaluated respectively. with ASpli (Mancini et al., 2021). GO term and KEGG pathway enrichment (adjusted P % 0.05, Fisher’s exact test) were performed in Panther (http://www.pantherdb.org/) and ShinyGO v.0.77 (http:// SUPPLEMENTAL INFORMATION bioinformatics.sdstate.edu/go/). Transcriptome datasets have been Supplemental information is available at Plant Communications Online. deposited at the Gene Expression Omnibus (https://www.ncbi.nlm.nih. gov/geo/) under accession number GSE226105. FUNDING This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Construction of co-abundance networks grant agreement no. 101028809 (AG-M), from project RTI2018-094350- Co-abundance networks were constructed for metabolites that exhibited B-C33 funded by the Spanish Ministry for Science, Innovation and Univer- significant differences in temporal trends of compound abundance under sities (VP), from grants SEV-2015-0533 and CEX2019-000902-S funded at least one condition (P % 0.05, linear mixed model). Random matrix the- by MCIN/AEI/10.13039/501100011033, and from the CERCA ory on pair-wise Pearson correlation matrices was used to determine a Programme/Generalitat de Catalunya. cutoff that avoided network saturation with a resolution of 0.01 using the RMThreshold package in RStudio. Node connectivity and community AUTHOR CONTRIBUTIONS composition according to the FastGreedy algorithm were computed using Conceptualization, A.G.-M.; methodology, A.G.-M. and V.P.; formal the Igraph package in RStudio. Networks were exported to Cytoscape analysis, A.G.-M. and V.P.; investigation, A.G.-M. and V.P.; resources, version 3.9.1 (Shannon et al., 2003) for visualization and manipulation. A.G.-M. and V.P.; data curation, A.G.-M.; writing – original draft, 12 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Metabolites modulate defense against pathogens Plant Communications A.G.-M. and V.P.; writing – review & editing, A.G.-M. and V.P.; project sequential biotic and abiotic stresses. Plant J. 86:249–267. https:// administration, A.G.-M.; funding acquisition, A.G.-M. and V.P. doi.org/10.1111/tpj.13167. Delplace, F., Huard-Chauveau, C., Berthomé, R., and Roby, D. (2022). ACKNOWLEDGMENTS Network organization of the plant immune system: from pathogen We acknowledge the Greenhouse Facility and Stress Program at CRAG perception to robust defense induction. Plant J. 109:447–470. for technical assistance and for providing biological material, and Dr. Cris- https://doi.org/10.1111/tpj.15462. tian Vicent (Servei Central d’Instrumentació Cientı́fica at UJI) for technical Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, assistance in metabolomics. Dr. Núria Sánchez-Coll (CRAG) is acknowl- S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: edged for critically reading our manuscript. We thank Dr. Vı́ctor Manuel ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. https:// González (CRAG) and Dr. Lisa van den Broeck (North Carolina State Uni- doi.org/10.1093/bioinformatics/bts635. versity, USA) for assistance in data analysis. The mutant lines tDT-1, pap1- D, and tt4-15 were a kind gift from Dr. Ekkehard Neuhaus (Technical Uni- Dyson, B.C., Miller, M.A.E., Feil, R., Rattray, N., Bowsher, C.G., versity Kaiserslautern, Germany) and Dr. Andreas Richter (University of Goodacre, R., Lunn, J.E., and Johnson, G.N. (2016). FUM2, a Rostock, Germany). Helena Kruyer is acknowledged for English editing cytosolic fumarase, is essential for acclimation to low temperature in and proofreading of the manuscript. This work is dedicated to the memory Arabidopsis thaliana. Plant Physiol. 172:118–127. https://doi.org/10. of Dr. Marı́a Coca. No conflict of interest is declared. 1104/pp.16.00852. Emmerlich, V., Linka, N., Reinhold, T., Hurth, M.A., Traub, M., Received: March 8, 2023 Martinoia, E., and Neuhaus, H.E. (2003). The plant homolog to the Revised: June 28, 2023 human sodium/dicarboxylic cotransporter is the vacuolar malate Accepted: June 29, 2023 carrier. Proc. Natl. Acad. Sci. USA 100:11122–11126. https://doi.org/ 10.1073/pnas.1832002100. Fernández-Bautista, N., Fernández-Calvino, L., Muñoz, A., Toribio, REFERENCES R., Mock, H.P., and Castellano, M.M. (2018). HOP family plays a € ning, B.A., Blankenberg, D., Goecks, J., Afgan, E., Nekrutenko, A., Gru major role in long-term acquired thermotolerance in Arabidopsis. Schatz, M.C., Ostrovsky, A.E., Mahmoud, A., Lonie, A.J., Syme, A., Plant Cell Environ. 41:1852–1869. https://doi.org/10.1111/pce.13326. et al. (2022). The Galaxy platform for accessible, reproducible and Finkemeier, I., Ko€ nig, A.C., Heard, W., Nunes-Nesi, A., Pham, P.A., collaborative biomedical analyses: 2022 update. Nucleic Acids Res. Leister, D., Fernie, A.R., and Sweetlove, L.J. (2013). 50:W345–W351. https://doi.org/10.1093/nar/gkac247. Transcriptomic analysis of the role of carboxylic acids in metabolite An, J., Kim, S.H., Bahk, S., Vuong, U.T., Nguyen, N.T., Do, H.L., Kim, S.H., signaling in Arabidopsis leaves. Plant Physiol. 162:239–253. https:// and Chung, W.S. (2021). Naringenin induces pathogen resistance doi.org/10.1104/pp.113.214114. against Pseudomonas syringae through the activation of NPR1 in Foyer, C.H., Bloom, A.J., Queval, G., and Noctor, G. (2009). Arabidopsis. Front. Plant Sci. 12. https://doi.org/10.3389/fpls.2021. Photorespiratory metabolism: Genes, mutants, energetics, and redox 672552. signaling. Annu. Rev. Plant Biol. 60:455–484. https://doi.org/10.1146/ Atkinson, N.J., Lilley, C.J., and Urwin, P.E. (2013). Identification of annurev.arplant.043008.091948. genes involved in the response of Arabidopsis to simultaneous biotic Gamir, J., Pastor, V., Kaever, A., Cerezo, M., and Flors, V. (2014). and abiotic stresses. Plant Physiol. 162:2028–2041. https://doi.org/ Targeting novel chemical and constitutive primed metabolites 10.1104/pp.113.222372. against Plectosphaerella cucumerina. Plant J. 78:227–240. https:// Balmer, A., Pastor, V., Glauser, G., and Mauch-Mani, B. (2018). doi.org/10.1111/tpj.12465. Tricarboxylates induce defense priming against bacteria in Garcia-Molina, A., Kleine, T., Schneider, K., Mu€ hlhaus, T., Lehmann, Arabidopsis thaliana. Front. Plant Sci. 9:1221. https://doi.org/10. M., and Leister, D. (2020). Translational components contribute to 3389/fpls.2018.01221. acclimation responses to high light, heat, and cold in Arabidopsis. Bolger, A.M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible iScience 23:101331. https://doi.org/10.1016/j.isci.2020.101331. trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. Griebel, T., Schu€ tte, D., Ebert, A., Nguyen, H.H., and Baier, M. (2022). https://doi.org/10.1104/pp.113.222372. Cold exposure memory reduces pathogen susceptibility in Arabidopsis Borevitz, J.O., Xia, Y., Blount, J., Dixon, R.A., and Lamb, C. (2000). based on a functional plastid peroxidase system. Mol. Plant Microbe Activation tagging identifies a conserved MYB regulator of Interact. 35:627–637. https://doi.org/10.1094/MPMI-11-21-0283-FI. phenylpropanoid biosynthesis. Plant Cell 12:2383–2394. https://doi. org/10.1105/tpc.12.12.2383. Gupta, A., Dixit, S.K., and Senthil-Kumar, M. (2016). Drought stress predominantly endures Arabidopsis thaliana to Pseudomonas Caldana, C., Degenkolbe, T., Cuadros-Inostroza, A., Klie, S., Sulpice, syringae infection. Front. Plant Sci. 7:808. https://doi.org/10.3389/ R., Leisse, A., Steinhauser, D., Fernie, A.R., Willmitzer, L., and fpls.2016.00808. Hannah, M.A. (2011). High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight Hannan Parker, A., Wilkinson, S.W., and Ton, J. (2022). Epigenetics: a environmental conditions. Plant J. 67:869–884. https://doi.org/10. catalyst of plant immunity against pathogens. New Phytol. 1111/j.1365-313X.2011.04640.x. 233:66–83. https://doi.org/10.1111/nph.17699. Charng, Y.Y., Mitra, S., and Yu, S.-J. (2023). Maintenance of abiotic Heil, M. (2002). Ecological costs of induced resistance. Curr. Opin. Plant stress memory in plants: Lessons learned from heat acclimation. Biol. 5:345–350. https://doi.org/10.1016/S1369-5266(02)00267-4. Plant Cell 35:187–200. https://doi.org/10.1093/plcell/koac313. Hillmer, R.A., Tsuda, K., Rallapalli, G., Asai, S., Truman, W., Papke, Chia, D.W., Yoder, T.J., Reiter, W.-D., and Gibson, S.I. (2000). Fumaric M.D., Sakakibara, H., Jones, J.D.G., Myers, C.L., and Katagiri, F. acid: an overlooked form of fixed carbon in Arabidopsis and other plant (2017). The highly buffered Arabidopsis immune signaling network species. Planta 211:743–751. https://doi.org/10.1007/s004250000345. conceals the functions of its components. PLoS Genet. 13:e1006639. https://doi.org/10.1371/journal.pgen.1006639. Coolen, S., Proietti, S., Hickman, R., Davila Olivas, N.H., Huang, P.-P., van Verk, M.C., van Pelt, J.A., Wittenberg, A.H.J., de Vos, M., Prins, van Hulten, M., Pelser, M., van Loon, L.C., Pieterse, C.M.J., and Ton, J. M., et al. (2016). Transcriptome dynamics of Arabidopsis during (2006). Costs and benefits of priming for defense in Arabidopsis. Proc. Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s). 13 Please cite this article in press as: Garcia-Molina and Pastor, Systemic analysis of metabolome reconfiguration in Arabidopsis after abiotic stressors un- covers metabolites that modulate defense against pathogens, Plant Communications (2023), https://doi.org/10.1016/j.xplc.2023.100645 Plant Communications Metabolites modulate defense against pathogens Natl. Acad. Sci. USA 103:5602–5607. https://doi.org/10.1073/pnas. deacclimation process in cold acclimated Arabidopsis thaliana. BMC 0510213103. Genom. 18:731. https://doi.org/10.1186/s12864-017-4126-3. Janda, M., Lamparová, L., Zubı́ková, A., Burketová, L., Martinec, J., and Prasch, C.M., and Sonnewald, U. (2013). Simultaneous application of  ková, Z. (2019). Temporary heat stress suppresses PAMP-triggered Krc heat, drought, and virus to Arabidopsis plants reveals significant immunity and resistance to bacteria in Arabidopsis thaliana. Mol. Plant shifts in signaling networks. Plant Physiol. 162:1849–1866. https:// Pathol. 20:1005–1012. https://doi.org/10.1111/mpp.12799. doi.org/10.1016/j.pbi.2021.102007. Jia, A., Huang, S., Ma, S., Chang, X., Han, Z., and Chai, J. (2023). TIR- Saijo, Y., and Loo, E.P.I. (2020). Plant immunity in signal integration catalyzed nucleotide signaling molecules in plant defense. Curr. Opin. between biotic and abiotic stress responses. New Phytol. Plant Biol. 73:102334. https://doi.org/10.1016/j.pbi.2022.102334. 225:87–104. https://doi.org/10.1111/nph.15989. Juszczak, I., Cvetkovic, J., Zuther, E., Hincha, D.K., and Baier, M. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., (2016). Natural variation of cold deacclimation correlates with Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: A software variation of cold-acclimation of the plastid antioxidant system in environment for integrated models of biomolecular interaction networks. Arabidopsis thaliana accessions. Front. Plant Sci. 7:305. https://doi. Genome Res. 13:2498–2504. https://doi.org/10.1101/gr.1239303. org/10.3389/fpls.2016.00305. Son, S., and Park, S.R. (2022). Climate change impedes plant immunity Kaever, A., Landesfeind, M., Feussner, K., Mosblech, A., Heilmann, I., mechanisms. Front. Plant Sci. 13:1032820. https://doi.org/10.3389/ Morgenstern, B., Feussner, I., and Meinicke, P. (2015). MarVis- fpls.2022.1032820. Pathway: integrative and exploratory pathway analysis of non- Suzuki, N., Rivero, R.M., Shulaev, V., Blumwald, E., and Mittler, R. targeted metabolomics data. Metabolomics 11:764–777. https://doi. (2014). Abiotic and biotic stress combinations. New Phytol. org/10.1007/s11306-014-0734-y. 203:32–43. https://doi.org/10.1111/nph.12797. Kaplan, F., Kopka, J., Haskell, D.W., Zhao, W., Schiller, K.C., Gatzke, Sweetlove, L.J., Beard, K.F.M., Nunes-Nesi, A., Fernie, A.R., and N., Sung, D.Y., and Guy, C.L. (2004). Exploring the temperature-stress Ratcliffe, R.G. (2010). Not just a circle: flux modes in the plant TCA metabolome of Arabidopsis. Plant Physiol. 136:4159–4168. https://doi. cycle. Trends Plant Sci. 15:462–470. https://doi.org/10.1016/j. org/10.1104/pp.104.052142. tplants.2010.05.006. Kaur, S., Tiwari, V., Kumari, A., Chaudhary, E., Sharma, A., Ali, U., and Taji, T., Ohsumi, C., Iuchi, S., Seki, M., Kasuga, M., Kobayashi, M., Garg, M. (2023). Protective and defensive role of anthocyanins under Yamaguchi-Shinozaki, K., and Shinozaki, K. (2002). Important plant abiotic and biotic stresses: An emerging application in roles of drought- and cold-inducible genes for galactinol synthase in sustainable agriculture. J. Biotechnol. 361:12–29. https://doi.org/10. stress tolerance in Arabidopsis thaliana. Plant J. 29:417–426. https:// 1016/j.jbiotec.2022.11.009. doi.org/10.1046/j.0960-7412.2001.01227.x. Less, H., Angelovici, R., Tzin, V., and Galili, G. (2011). Coordinated gene Vyse, K., Pagter, M., Zuther, E., and Hincha, D.K. (2019). Deacclimation networks regulating Arabidopsis plant metabolism in response to after cold acclimation—a crucial, but widely neglected part of plant various stresses and nutritional cues. Plant Cell 23:1264–1271. winter survival. J. Exp. Bot. 70:4595–4604. https://doi.org/10.1093/ https://doi.org/10.1105/tpc.110.082867. jxb/erz229. Liao, Y., Smyth, G.K., and Shi, W. (2014). featureCounts: an efficient general Wang, Y., Bao, Z., Zhu, Y., and Hua, J. (2009). Analysis of temperature purpose program for assigning sequence reads to genomic features. modulation of plant defense against biotrophic microbes. Mol. Plant Bioinformatics 30:923–930. https://doi.org/10.1093/bioinformatics/ Microbe Interact. 22:498–506. https://doi.org/10.1094/MPMI-22-5-0498. btt656. Weßling, R., Epple, P., Altmann, S., He, Y., Yang, L., Henz, S., Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of McDonald, N., Wiley, K., Bader, K., Gla € ßer, C., et al. (2014). fold change and dispersion for RNA-seq data with DESeq2. Genome Convergent targeting of a common host protein-network by Biol. 15:550. https://doi.org/10.1186/s13059-014-0550-8. pathogen effectors from three kingdoms of life. Cell Host Microbe Mancini, E., Rabinovich, A., Iserte, J., Yanovsky, M., and 16:364–375. https://doi.org/10.1094/MPMI-22-5-0498. Chernomoretz, A. (2021). ASpli: integrative analysis of splicing Wilkinson, S.W., Hannan Parker, A., Muench, A., Wilson, R.S., landscapes through RNA-Seq assays. Bioinformatics 37:2609–2616. Hooshmand, K., Henderson, M.A., Moffat, E.K., Rocha, P.S.C.F., https://doi.org/10.1093/bioinformatics/btab141. Hipperson, H., Stassen, J.H.M., et al. (2023). Long-lasting memory Mukhtar, M.S., Carvunis, A.-R., Dreze, M., Epple, P., Steinbrenner, J., of jasmonic acid-dependent immunity requires DNA demethylation Moore, J., Tasan, M., Galli, M., Hao, T., Nishimura, M.T., et al. and ARGONAUTE1. Nat. Plants 9:81–95. https://doi.org/10.1038/ (2011). Independently evolved virulence effectors converge onto s41477-022-01313-9. hubs in a plant immune system network. Science 333:596–601. Xu, D., Dhiman, R., Garibay, A., Mock, H.P., Leister, D., and Kleine, T. https://doi.org/10.1093/bioinformatics/btab141. (2020). Cellulose defects in the Arabidopsis secondary cell wall Ngou, B.P.M., Ding, P., and Jones, J.D.G. (2022). Thirty years of promote early chloroplast development. Plant J. 101:156–170. resistance: Zig-zag through the plant immune system. Plant Cell https://doi.org/10.1111/tpj.14527. 34:1447–1478. https://doi.org/10.1093/bioinformatics/btab141. Yeh, C.-H., Kaplinsky, N.J., Hu, C., and Charng, Y.Y. (2012). Some like it hot, € urle, I. (2021). Epigenetic regulation of Oberkofler, V., Pratx, L., and Ba some like it warm: Phenotyping to explore thermotolerance diversity. Plant abiotic stress memory: maintaining the good things while they last. Sci. 195:10–23. https://doi.org/10.1016/j.plantsci.2012.06.004. Curr. Opin. Plant Biol. 61:102007. https://doi.org/10.1016/j.pbi.2021. Zarattini, M., Farjad, M., Launay, A., Cannella, D., Soulié, M.C., 102007. Bernacchia, G., and Fagard, M. (2021). Every cloud has a silver lining: O’Connell, R.J., Thon, M.R., Hacquard, S., Amyotte, S.G., Kleemann, how abiotic stresses affect gene expression in plant-pathogen J., Torres, M.F., Damm, U., Buiate, E.A., Epstein, L., Alkan, N., interactions. J. Exp. Bot. 72:1020–1033. https://doi.org/10.1093/jxb/ et al. (2012). Lifestyle transitions in plant pathogenic Colletotrichum eraa531. fungi deciphered by genome and transcriptome analyses. Nat. Zuther, E., Juszczak, I., Lee, Y.P., Baier, M., and Hincha, D.K. (2015). Genet. 44:1060–1065. https://doi.org/10.1038/ng.2372. Time-dependent deacclimation after cold acclimation in Arabidopsis Pagter, M., Alpers, J., Erban, A., Kopka, J., Zuther, E., and Hincha, thaliana accessions. Sci. Rep. 5:12199. https://doi.org/10.1038/ D.K. (2017). Rapid transcriptional and metabolic regulation of the srep12199. 14 Plant Communications 4, 100645, November 13 2023 ª 2023 The Author(s).