FLS2‐RBOHD module regulates changes in the metabolome of Arabidopsis in response to abiotic stress

Abstract Through crosstalk, FLAGELLIN SENSITIVE 2 (FLS2) and RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) are involved in regulating the homeostasis of cellular reactive oxygen species (ROS) and are linked to the metabolic response of plants toward both biotic and abiotic stress. In the present study, we examined the metabolome of Arabidopsis seedlings under drought and salt conditions to better understand the potential role of FLS2 and RBOHD‐dependent signaling in the regulation of abiotic stress response. We identified common metabolites and genes that are regulated by FLS2 and RBOHD, and are involved in the response to drought and salt stress. Under drought conditions, D‐aspartic acid and the expression of associated genes, such as ASPARAGINE SYNTHASE 2 (ASN2), increased in both fls2 and robed/f double mutants. The accumulation of amino acids, carbohydrates, and hormones, such as L‐proline, D‐ribose, and indoleacetaldehyde increased in both fls2 and rbohd/f double mutants under salt conditions, as did the expression of related genes, such as PROLINE IMINOPEPTIDASE, PHOSPHORIBOSYL PYROPHOSPHATE SYNTHASE 5, and NITRILASE 3. Collectively, these results indicate that the FLS2‐RBOHD module regulates plant response to drought and salt stress through ROS signaling by adjusting the accumulation of metabolites and expression of genes related to metabolite synthesis.


| INTRODUC TI ON
During growth and development, plants continuously interact with the environment during their growth and development, inducing the synthesis of many different primary and secondary metabolites (Yang et al., 2018). Different metabolites are specifically or non-specifically expressed in different developmental stages and tissues of plants, as well as in response to a variety of biotic and abiotic stresses (Isah, 2019). In this regard, drought and salt stress can have a significant negative impact on plant growth and development, and also induce dynamic changes in the level of different metabolites (Arif et al., 2020;Kumar et al., 2021). This includes | 37 YU et al. metabolites such as amino acids, carbohydrates, and also secondary metabolites, such as phenolic acids and flavonoids, terpenes and steroids, and alkaloids (Isah, 2019;Singh et al., 2020). Notably, studies have indicated that these metabolites play a key role in plant adaptation to biotic and abiotic stresses (Fàbregas & Fernie, 2019).
A recent study reported a higher accumulation of L-aspartic acid in leaves of tolerant varieties of chickpea (Cicer arietinum L.) under drought stress conditions, indicating that L-aspartic acid may serve as a marker metabolite for drought response (Khan et al., 2019). The concentration of soluble protein and proline, as well as superoxide dismutase and catalase activity, was also found to be elevated in Apiaceae (Bupleurum chinense DC.) in response to drought stress . Collectively, studies have shown that the synthesis of a series of metabolites is induced as part of plant response to adverse conditions. FLAGELLIN SENSITIVE 2 (FLS2) and RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) function in regulating cytosolic calcium and the production of reactive oxygen species (ROS) during the defense response in plants (Chi et al., 2021;Li et al., 2014;Melotto et al., 2006). Receptor-like cytoplasmic kinase BIK1 (BOTRYTIS-INDUCED KINASE 1), one of the components of the FLS2 immunoreceptor complex, positively regulates the accumulation of ROS by directly phosphorylating RBOHD . FLS2 forms a functional complex with the BRASSINOSTEROID INSENSITIVE 1 (BRI1)-associated kinase receptor 1 (BAK1), resulting in Ca 2+ influx and ROS production (Chinchilla et al., 2007). Abiotic stresses, such as salt stress, can also enhance the accumulation of ROS in plants by activating RBOHD (Luo et al., 2021). ROS accumulation and the activation of the Ca 2+ signaling pathway are known to be involved in abiotic stress response in plants (Bush, 1995;Yu et al., 2002).
Notably, our recent study revealed the essential roles of the FLS2-RBOHD-PIF4 module in regulating the adaptive response of plants to drought and salt stress (Liu et al., 2022). Although 2 | E XPERIMENTAL PROCEDURE S

| Plant material and growth conditions
The WT is Columbia (Col-0). The fls2 (SALK_141277) and rbohd/f double mutants (CS9558) in Col-0 background were obtained from the Arabidopsis Biological Resource Center (Table S1). Homozygous T-DNA insertion lines were confirmed by polymerase chain reaction (PCR) using gene-specific and T-DNA-specific primers (Table S2; Figure S3). For NaCl treatments, 1-week-old seedlings were first transplanted into the soil to grow for 1 week under normal growth conditions. Subsequently, these were watered with an aqueous solution containing 100 mM NaCl and allowed to grow further for 1 week. For drought treatment, the seedlings were first transplanted into normal watered soil; afterward, watering was stopped after transplantation. After 1 week, the soil water content decreased to about 10%, and the seedlings were further grown for 1 week. For control, seedlings of the same batch were transplanted into the soil and grown under normal watering conditions (watering once a week) for 3 weeks.

| RNA-seq analysis
Total RNA was extracted using the mirVana miRNA isolation kit (Ambion) following the manufacturer's protocol. RNA integrity was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies).
The samples with RNA Integrity Number (RIN) ≥ 7 were subjected to subsequent RNA-seq analysis. The libraries were constructed using TruSeq Stranded mRNA LTSample Prep Kit (Illumina) according to the manufacturer's instructions. Libraries were sequenced on the Illumina sequencing platform (HiSeqTM 2500 or Illumina HiSeq X Ten), and 125-bp/150-bp paired-end reads were generated. Then, raw data (raw reads) were processed using Trimmomatic. The reads containing ploy-N and the low-quality reads were removed to obtain clean reads. Then, the clean reads were mapped to the reference genome using hisat2. After that, the FPKM value of each gene was calculated using cufflinks, and the read counts of each gene were obtained by htseq-count. DEGs were identified using the DESeq (2012) R package functions estimateSizeFactors and nbinomTest.
p-value <.05 and fold change >2 or fold change <.5 were set as the threshold for significantly differential expression. Hierarchical cluster analysis of DEGs was performed to explore gene expression patterns. RNA sequence data are available at https://datav iew.ncbi.nlm.

| LC-MS analysis
All chemicals and solvents were analytical or HPLC grade. Water, methanol, acetonitrile, and formic acid were purchased from CNW Technologies GmbH. L-2-chlorophenylalanine was purchased from Shanghai Hengchuang Bio-technology Co., Ltd.

| Sample preparation
Transfer 80 mg of accurately weighed sample to a 1.5 mL Eppendorf tube. Two small steel balls were added to the tube. 20 μL of 2-chloro-l-phenylalanine (0.3 mg/mL) dissolved in methanol as internal standard and a 1 mL mixture of methanol and water (7/3, vol/vol) were added to each sample, samples were placed at −80°C for 2 min. Then grinded at 60 HZ for 2 min, and ultrasonicated at ambient temperature for 30 min after vortexed, then placed at 4°C for 10 min. Samples were centrifuged at 13,000 rpm, 4°C for 15 min. Supernatant in a brown and glass vial was dried in a freeze concentration centrifugal dryer. A mixture of methanol and water (1/4, vol/vol) were added to each sample, samples vortexed for 30 s, then placed at 4°C for 2 min. Samples were centrifuged at 13,000 rpm, 4°C for 5 min. The supernatants (150 μL) from each tube were collected using crystal syringes, filtered through 0.22 μm microfilters, and transferred to LC vials.
The vials were stored at −80°C until LC-MS analysis. QC samples were prepared by mixing aliquots of all samples to be a pooled sample.

| Sample on-board processing
An ACQUITY UHPLC system (Waters Corporation) coupled with an AB SCIEX Triple TOF 5600 System (AB SCIEX) was used to analyze the metabolic profiling in both ESI-positive and ESInegative ion modes. An ACQUITY UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm) was employed in both positive and negative modes.
All the samples were kept at 4°C during the analysis. The injection volume was 2 μL. Data acquisition was performed in full scan mode (m/z ranges from 70 to 1000) combined with IDA mode. The QCs were injected at regular intervals (every 10 samples) throughout the analytical run to provide a set of data from which repeatability can be assessed. Mass spectrum conditions: ESI was used as ion source. Positive and negative ion scanning modes were used to collect the sample quality spectrum signals. The mass spectrum parameters are shown in Table 1.

| LC-MS data preprocessing and statistical analysis
The acquired LC-MS raw data were analyzed by the progenesis QI software (Waters Corporation) using the following parameters.
The resulting matrix was further reduced by removing any peaks with a missing value (ion intensity = 0) in more than 50% samples. The internal standard was used for data QC (quality control) scaling, respectively. PCA is to explore the degree of correlation among multiple possible correlation variables, find the maximum or minimum correlation direction, and achieve the purpose of data compression or noise reduction (dimension reduction). OPLS-DA analysis combines orthogonal signal correction and PLS-DA methods to decompose the X matrix into two types of information related and unrelated to Y, and screen the differential variables by removing the unrelated differences. The abscissa and ordinate of the PCA graph represent the projected score values of each sample on the PC1 (principal components 1) and PC2, respectively (Nicholson et al., 1999). The projected score value of each sample on the plane is composed of the first principal component and the   second principal component is the spatial coordinate, which can intuitively reflect the similarity or difference between the samples (Okada et al., 2010). The closer the distance between different samples indicates the closer the composition and concentration of the molecules they contain (Jolliffe & Cadima, 2016). On the OPLS-DA graph, there are two principal components, the predicted PC1 and the orthogonal principal component (PCo1) (Trygg & Wold, 2002). OPLS-DA maximizes the differences between groups and reflects the PC1, so the differences between groups can be directly distinguished from the PC1, while the PCo1 reflects the intra-group differences (Trygg & Wold, 2002). The Hotelling's T2 region, shown as an ellipse in score plots of the models, defines the 95% confidence interval of the modeled variation. Variable importance in the projection (VIP) ranks the overall contribution of each variable to the OPLS-DA model, and those variables with VIP >1 are considered relevant for group discrimination. In this study, the default 7-round cross-validation was applied with one-seventh of the samples being excluded from the mathematical model in each round, in order to guard against overfitting.
The differential metabolites were selected based on the combina-

| Association analysis of metabolome and transcriptome
Metabolite synthesis-related genes were found according to the metabolic pathways involved in metabolites in the KEGG: https:// www.kegg.jp/. The genes involved in metabolite-related metabolic pathways were compared with the transcriptome data obtained by RNA-seq to obtain the expression levels of genes in different samples. The heat map of gene expression was drawn using the website of Oebiotech: https://cloud.oebio tech.com/task/detai l/heatm ap/.
And the GO analyses were performed on Metascape: http://metas cape.org (Zhou et al., 2019). Firstly, the metabolite synthesis-related genes list was uploaded on Metascape according to the operating manual. After uploading the data, GO analyses were performed on Metascape automatically. After the analysis was completed, the results of GO were downloaded from Metascape.

| LC-MS analysis of fls2 mutant and rbohd/f double mutant under no-stress (CK), salt, and drought stress conditions
We previously demonstrated that an Arabidopsis rbohd/f double mutant exhibited sensitivity to drought and salt stress, relative to wild-type (WT) and fls2 mutant plants which exhibited tolerance to both stresses (Liu et al., 2022). Therefore, we analyzed A PCA diagram illustrates that the samples clustered together, and therefore indicates that there was little difference between samples ( Figure S2). The metabolomic data obtained in the experiment were multidimensional and some of the variables were highly correlated. Therefore, we utilized a multivariate statistical analysis to identify DAMs between different comparison groups.
Principal component analysis was first used to observe the overall distribution of DAMs in different samples ( Figure S1b). Then, orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to determine differences between samples ( Figure S1c). DAMs with biological significance were thus identified through the OPLS-DA analysis. In addition, we also used fold change to further confirm the significance of the DAMs between comparison groups (Figures 2a, 4a, and 6a; Figure S5a). that were assigned to 26 metabolic pathways mainly associated with plant development, including: "arginine biosynthesis," "galactose metabolism," and "citrate cycle (TCA cycle)" (Figure 1a,b).
Next, we analyzed changes in the abundance of these metabolites. Results indicated that the number of amino acids and carbohydrates with increased abundance were greater in fls2_NaCl than in WT_NaCl, including L-proline, L-glutamic acid, and maltose found that the abundance of indole acetaldehyde was significantly higher in the "fls2_NaCl versus WT_NaCl" comparison.
An association analysis of the transcriptome and metabolome data was then performed to determine if changes in the expression of genes related to the DAMs were following each other.
As shown in Figure 2c, we focused on DAMs such as L-aspartic acid, L-proline, L-glutamic acid, L-methionine, sucrose, maltose, epicatechin, indoleacetaldehyde, guanine, and cytosine in the correlation analysis (Figure 2c). The expression data (transcriptome) of metabolite-related genes were used to construct a heatmap and GO process chart (Figure 2c,d). The results indicated that in "fls2_ NaCl versus WT_NaCl," "fls2_CK versus WT_CK," "fls2_NaCl versus stress, relative to WT plants ( Figure S1b). A total of 45 DAMs were identified in the "rbohd/f_NaCl versus WT_NaCl" comparison group that were assigned to 18 metabolic pathways, including "arginine and proline metabolism," "alanine, aspartate and glutamate metabolism," and "riboflavin metabolism" (Figure 3a,b). We also found that the number of carbohydrate metabolites that decreased in abundance was higher than the number of carbohydrate metabolites with increased abundance in the "rbohd/f_CK versus WT_CK" comparison group (Figure 4b).

| Fls2 is involved in sensing drought stress signals
Plants subjected to drought stress accumulate a variety of organic and inorganic substances, including sugars, amino acids, and inorganic ions, to increase the osmotic potential of their cells and enhance their water retention capacity (Rhodes & Samaras, 1994). comparison that were assigned to 14 metabolic pathways. These included "glyoxylate and dicarboxylate metabolism," "alanine, aspartate, and glutamate metabolism," and the "citrate cycle (TCA cycle)" ( Figure 5a,b).
Analysis of the "fls2_CK versus WT_CK" and "fls2_Drought versus WT_Drought" comparison groups indicated that the number of amino acids with increased abundance was lower in fls2_Drought plants, while the number of flavonoid metabolites and carbohydrates with higher abundance increased in the "fls2_Drought versus WT_Drought" comparison group (Figure 6a,b), including L-lyxonate and epicatechin. Additionally, the number of amino acids and carbohydrates with increased abundance was lower in the "fls2_Drought versus fls2_CK" comparison group than it was in the "WT_Drought versus WT_CK" comparison group (Figure 6b). Unlike fls2_NaCl samples, no increase in the accumulation of indoleacetaldehyde was observed in fls2_Drought samples (Figures 2a and 6a).
An association analysis of metabolome and transcriptome data was performed for the DAMs of D-aspartic acid (DAA), L-glutamic acid, D-glucose 1-phosphate, maltose, epicatechin, guanine, and uracil in "fls2_Drought versus fls2_CK," "fls2_CK versus WT_CK," F I G U R E 4 Identification and analysis of the differentially abundant metabolites (DAMs) in different comparison groups under CK and salt stress conditions. (a) Volcano plots of DAMs were identified in the various comparisons of rbohd/f and WT samples under CK and salt stress conditions. DAMs were determined based on p-value and fold change (p-value < .05, |log2FC| > .58). Red dots indicate metabolites with significantly increased abundance, green dots represent metabolites with significantly decreased abundance, and gray dots indicate a change in metabolites that were nonsignificant. (b) Bar chart of the number of metabolites in different categories of metabolites in the "rbohd/f_NaCl versus WT_NaCl," "rbohd/f_CK versus WT_CK," "WT_NaCl versus WT_CK," and "rbohd/f_NaCl versus rbohd/f _CK" comparison groups. The height of the blue bars represents the number of metabolites with increased abundance in the different comparison groups.  Drought samples (Figure 6c). Furthermore, the expression of genes related to maltose synthesis was significantly upregulated in fls2_ Drought samples (Figure 6c,d). The expression of genes related to epicatechin, D-glucose 1-phosphate, and DAA, such as ASPARTATE AMINO TRA NSF ERASE 1 (ASP1) and PHOSP HOG LUC OMU TASE 2 (PGM2), were also upregulated in WT_CK samples (Figure 6c). The analysis of "rbohd/f_Drought versus rbohd/f_CK" and "WT_Drought versus WT_CK" comparison groups indicated that the number of amino acids, carbohydrates, and flavonoids with increased abundance significantly increased in rbohd/f_Drought samples ( Figure S5b). In contrast, the number of purines and pyrimidines with increased abundance were observed to decrease in rbohd/f_ Drought samples ( Figure S5a,b). These results indicate that the abundance of a great number of metabolites in the rbohd/f double mutant changes under drought stress conditions. Additionally, the analysis of the "rbohd/f_CK versus WT_CK" and "rbohd/f_Drought versus WT_Drought" comparison groups indicated that the number of amino acids, flavonoids, and carbohydrate metabolites with increased abundance was significantly higher in rbohd/f_Drought samples ( Figure S5b).

An association analysis of metabolome and transcriptome data
for DAMs was conducted that include the metabolites L-proline, L-glutamic acid, sucrose, indoleacetic acid, AMP, and UDP-Dgalactose. As shown in Figure S5c, results indicated that the expression of genes related to the synthesis of L-glutamic acid, sucrose, L-proline, and indoleacetic acid was higher in rbohd/f samples than in WT samples under drought stress conditions. For example, the level of the indoleacetic acid-related genes NITRILASE 2 (NIT2) and SULFO TRA NSF ERASE 16 (SOT16) were significantly higher in rbohd/f_CK and rbohd/f_Drought samples. The level of expression of genes related to AMP and UDP-D-galactose synthesis was higher in WT_CK samples than in rbohd/f_CK samples ( Figure S5a,c).

| Expression analysis of antioxidant defenserelated genes in fls2 and rbohd/f mutants
The activity and abundance of several antioxidant proteins in plants are enhanced when they are exposed to abiotic stress to provide protection from oxidative injury to cells (Gill & Tuteja, 2010). Thus, vitamin E, cytochrome f, and anthocyanins (Noctor, 2005).
We also analyzed the expression level and GO enrichment of genes in the fls2 mutant, rbohd/f double mutant, and WT samples that were related to antioxidant defense metabolites ( Figure S6a,b).
The level of many genes related to antioxidant defense in rbohd/f double mutant samples was mostly higher than they were in WT under both stress and CK conditions. For example, the level of expression of GLUTATHIONE S-TRANSFERASE F3 (GSTF3) and MONOD EHY DRO ASC ORBATE REDUCTASE 1 (MDAR1) was higher in rbohd/f_Drought and rbohd/f_NaCl samples than in WT_Drought and WT_NaCl samples ( Figure S6a). Interestingly, the level of expression of genes related to antioxidative stress in the fls2 mutant did exhibit significant differences from the WT under either NaCl or drought conditions ( Figure S6a).

| Fls2-RBOHD module co-regulates the expression of key metabolites
We further analyzed the expression of identical metabolites and re-

| FLS2 regulates the response of plants to drought and salt stress by modulating metabolite levels in plants
Plants are inevitably affected by abiotic stresses such as drought, salinity, or high and low temperatures, which greatly limits their growth and development (Nadarajah, 2020). FLS2 is a kinase that functions as a receptor of flg22, a conserved 22 amino acid peptide of plantbacterial pathogens (Zipfel et al., 2004). Our previous study revealed the important role that FLS2 also plays in regulating the response of plants to abiotic stresses (Liu et al., 2022). In the present study, we further examined the potential effect of FLS2 on the accumulation of metabolites in plants subjected to salt and drought stress. Our results revealed an increased abundance of amino acids and carbohydrates, such as L-proline, L-isoleucine, D-ribose, and others in the "fls2_NaCl versus WT_NaCl" comparison group (Figure 2a,b). L-proline contributes to maintaining the homeostasis of the intracellular redox environment and intracellular ROS content, both of which have been shown to be associated with abiotic stress tolerance (Krishnan et al., 2008;Szabados & Savouré, 2010). Carbohydrates are important carbon energy reserves and are also involved in intracellular redox homeostasis (Poltronieri et al., 2011). The substantial accumulation of metabolites in fls2_NaCl mutant plants is due to the sensitivity of fls2 mutant to salt stress. FLS2 is a receptor kinase, which suggests that it may be involved in the perception of salt stress, a premise indicated in our previous study (Liu et al., 2022). The sensitivity of fls2 mutant to salt stress may be due to the defective receptor kinases in fls2 mutant that sense abiotic stress. Notably, in the analysis of the "fls2_Drought versus fls2_CK" comparison group, no significant accumulation of amino acids or carbohydrates was observed in fls2_Drought samples (Figure 6a,b). Based on these data, we speculate that this may indicate that fls2 mutant does not have a strong sensitivity to drought stress but rather are likely to be tolerant to drought conditions. acid has been reported to regulate root development in response to many abiotic stresses, including salt stress (Korver et al., 2018).

| rbohd/f
Our results indicated that the abundance of DAMs such as dAMP and UDP-D-galactose and others decreased in rbohd/f_NaCl samples, relative to WT_NaCl samples (Figure 4a,b). In this regard,

ROS accumulation has been reported to have a negative impact on
sugar and base moieties and results in oxidative damage to DNA (Boiteux et al., 2017). Consequently, this may lead to a decrease in the abundance of metabolites such as dAMP under salt stress conditions. Our results suggest that ROS signaling mediated by RBOHD/F induces dynamic changes in metabolite production in plants under salt stress.
In the analysis of the "rbohd/f_Drought versus rbohd/f_CK" and "WT_Drought versus WT_CK" comparison groups, the abundance of DAMs such as indoleacetic acid, L-glutamic acid, L-aspartic acid, sucrose, and others was significantly greater in rbohd/f_Drought samples than in the other samples ( Figures S4a and S5a,b).
Extensive amino acid accumulation has often been observed in plants growing under abiotic stress conditions, including maize, cotton, Arabidopsis thaliana, and others (Huang & Jander, 2017;Ranieri et al., 1989;Showler, 2002). Additionally, the accumulation of indoleacetic acid is known to have a significant effect on plant growth and stress tolerance (Yemelyanov et al., 2020). In the analysis of the "rbohd/f_CK versus WT_CK" comparison group, the number of carbohydrates that decreased in abundance was high

| Combined metabolome and transcriptome analysis of the effect of FLS2 and RBOHD on regulating the abundance of stress-induced metabolites and the expression of associated genes
We conducted a combined metabolome and transcriptome analysis to examine the relationship between DAMs and the expression of their related genes in fls2 and rbohd/f double mutants under drought and NaCl stress conditions (Figures 2c, 4c, and 6c; Figure S5c). The analysis revealed that many genes, including GSTF2, ASN2, YUCCA 8 (YUC8), GLUCOSIDE GLUCOHYDROLASE 2 (TGG2), and others, that were related to the identified DAMs were significantly upregulated in fls2_NaCl samples, relative to WT_NaCl samples, suggesting that the fls2 mutant was more sensitive to NaCl than WT plants ( Figure 2c). Glutathione S-transferase has been reported plays an important role in maintaining redox homeostasis and reducing oxidative damage (Chen et al., 2012), and GSTF2 is also closely related to the regulation of oxidative stress response in Arabidopsis (Lee et al., 2014). ASN2 has been shown to play an important role in the response of Arabidopsis to salt stress (Maaroufi-Dguimi et al., 2011).
TGG2 can decrease the accumulation of ROS in Arabidopsis through its antioxidant properties (Zhao et al., 2015). The upregulation of these genes, however, may indicate the increased sensitivity of fls2 mutant to NaCl (Figure 2c). Although maltose-related genes exhibited increased expression in fls2_Drought samples (Figure 6c), other genes such as GSTF7 were also highly expressed in WT_CK samples, and thus, did not exhibit a significant increase in fls2_Drought samples ( Figure 6b). This is consistent with the metabolome data for fls2 mutant indicating that they exhibited tolerance to drought stress  . FLS2 also plays a key role in plant tolerance to abiotic stress by controlling flavonol accumulation .
Our results suggest that the rbohd/f double mutant is more sensitive to drought and salt stress, and that both RBOHD and FLS2 are required for plant response and adaptation to abiotic stress.
CAT2 has been reported to play an important role in ROS scavenging in plants under abiotic stress conditions (Ono et al., 2021) and GPX1 is known to be involved in the detoxification of H 2 O 2 (Avsian- Kretchmer et al., 2004). These genes, however, did not exhibit a specific increase in expression in the fls2 mutant ( Figure S6). These Both biotic and abiotic stresses can lead to the accumulation of intracellular ROS. Thus, plants respond to both types of stresses by regulating changes in intracellular ROS (Fichman & Mittler, 2020).
FLS2 and RBOHD are closely related to the ability of RBOHD to induce ROS and Ca 2+ signaling, which play an essential role in regulating plant response to abiotic stresses (Noirot et al., 2014). In conclusion, our study revealed the potential role of FLS2 and RBOHD in regulating the abundance of DAMs in response to drought and salt stress conditions and the expression of DAM-related genes. We provide evidence that the metabolites and genes controlled by both FLS2 and RBOHD regulate ROS and Ca 2+ signaling in plants subjected to abiotic stress. Our results reveal the potential role of FLS2 and RBOHD in the regulation of the response of a higher plants to abiotic stresses and also provide new strategies for the combined analysis of metabolomic and transcriptomic data (Figure 8).
F I G U R E 8 Proposed model of FLAGELLIN SENSITIVE 2 (FLS2) and RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) signaling in response to abiotic stress conditions. FLS2 and RBOHD regulate the synthesis of differentially abundant metabolitesthrough reactive oxygen species signaling when plants are subjected to drought and salt stress, which includes the participation of related enzymes. RBOHD also regulates the synthesis of antioxidant enzymes in plants and the expression of their related genes, enabling plants to respond to abiotic stresses.

ACK N OWLED G M ENTS
We are grateful to ABRC for the Arabidopsis seeds.

CO N FLI C T O F I NTE R E S T S TATE M E NT
The authors declare no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
All data supporting the findings of this study are available within the paper and within its supplementary data published online.