Microbial volatiles organic compounds (mVOCs) play diverse roles in modulating plant growth and stress tolerance. However, the molecular responses of plants to mVOCs are largely undescribed. In this study, we examined the early transcriptomic response of Arabidopsis thaliana to two plant growth-promoting mVOCs (PGPVs) and one plant growth-inhibiting mVOC (PGIV). Our phenotype analysis showed that PGPVs from Fusarium verticillioides and Simplicillium sympodiophorum promote plant growth by affecting different organs. In particular, F. verticillioides mVOCs promote plant growth in whole seedlings, while S. sympodiophorum mVOCs increase leaf surface area. Moreover, Arabidopsis treated with the two PGPVs exhibited different growth-associated molecular responses, which corresponded to the phenotype analysis results. For instance, the FAR1 family (regulates light-associated plant development) was upregulated by F. verticillioides mVOCs, while the LBD family (regulates leaf size and shape) was enriched among S. sympodiophorum mVOC-upregulated genes. Hierarchical clustering analysis further indicated that PGPVs induced expression of growth-associated genes and suppressed expression of defense-associated genes. In contrast to the PGPV-induced transcriptional effects, PGIVs caused downregulation of growth-associated genes with coincident upregulation of defense-associated genes. Furthermore, a transcription factor (TF) enrichment analysis suggested that HSFs, NACs and WRKYs might be core regulators in the plant response towards mVOCs. In particular, WRKYs might serve as integrating nodes to regulate salicylic acid- and jasmonic acid-mediated defense responses and growth-defense trade-offs. Overall, this study provides insights into the early molecular responses of plants after mVOC exposure and suggests that these molecular responses contribute to different phenotypic responses.

1 INTRODUCTION

As sessile organisms, plants have evolved intricate signaling networks that mediate specific responses to environmental stimuli and signals from other organisms. Among these responses, plant reactions to different microbes are essential determinants of plant resilience in the face of environmental stresses (De Zelicourt et al., 2013). A major mechanism of microbe sensing by plants involves microbial volatile compounds (mVOCs), and plant responses to mVOCs have been widely characterized in recent decades. Nevertheless, it still remains largely unknown how different plant cells respond to different mVOCs at a molecular level.

mVOCs are microbial secondary metabolites emitted in a gaseous form, which often serve as signals in plant-microbe and/or microbe-microbe interactions (Ryu et al., 2003; Schulz-Bohm et al., 2017). The blend of mVOCs emitted by a single microbe may contain a variety of metabolites, including alkenes, alcohols, sulfur compounds, terpenoids, and other types of molecules (Kanchiswamy et al., 2015). Different mVOCs are known to stimulate different physiological responses in plants, such as growth modulation, enhancement of nutrient uptake and promotion of stress resilience (Schulz-Bohm et al., 2017; Sharifi et al., 2022). mVOCs may also induce systemic resistance and prime a plant's tolerance to biotic and abiotic stress (Zamioudis et al., 2015; Cappellari and Banchio 2020; Yasmin et al., 2021; Pescador et al., 2022; Chang et al., 2023; Chiang et al., 2023).

Based on their functions, mVOCs may be classified as plant growth-promoting mVOCs (PGPVs) or plant growth-inhibiting mVOCs (PGIVs). These designations are often assigned by evaluating plant growth at different ages after exposure to mVOCs. Some PGPVs are known to promote plant growth by modulating signaling of phytohormones, such as gibberellins (GB), cytokinins (CK), brassinosteroids (BR), auxins (IAA), ethylene (ET) and abscisic acid (ABA; Bhattacharyya et al., 2015; Cappellari and Banchio, 2020; Gao et al., 2022; García-Gómez et al., 2020; Kamaruzzaman et al., 2021; Ryu et al., 2003). In addition, PGPVs may promote plant growth by enhancing the uptake of nutrients (e.g., iron and sulfur) or by increasing photosynthesis activity (Castulo-Rubio et al., 2015; Esparza-Reynoso et al., 2021; Meldau et al., 2013; Zhang et al., 2009). In contrast to the range of reports on PGPVs, studies on PGIV-mediated suppression of plant growth are comparatively rare. Nevertheless, WRKY18 has been identified as a key modulator of Arabidopsis growth after PGIV exposure (Wenke et al., 2012). Moreover, previous work also showed that auxin response and the activities of auxin transporters may be suppressed by PGIV exposure (Chang et al., 2023).

Both PGPVs and PGIVs have been shown to prime plant tolerance to biotic and abiotic stresses (Cappellari and Banchio, 2020; Chang et al., 2023; Chiang et al., 2023; Pescador et al., 2022; Yasmin et al., 2021; Zamioudis et al., 2015). For instance, the combination of PGPVs from Pseudomonas fluorescens WCS417 with photosynthesis-associated signals induces systemic resistance in Arabidopsis through MYB72 (Zamioudis et al., 2015). Defense-associated phytohormones like jasmonic acid (JA), salicylic acid (SA) and ethylene (ET) are also known to be involved in different mVOC-induced priming effects (Cho et al., 2008; Rudrappa et al., 2010; Ryu et al., 2004). As such, the mVOC 2,3-butanediol primes Arabidopsis disease resistance via actions involving JA and SA, while SA and ET are essential to acetoin-induced priming in Arabidopsis (Rudrappa et al., 2010; Ryu et al., 2004). In addition, MAPK cascades are crucial immune-related signaling pathways (Ngou et al., 2024), and our previous work showed that MKK1 and MKK3 may act as central regulators of a PGIV-induced defense response (Chang et al., 2023). Furthermore, volatiles from Pseudomonas chlororaphis O6 improve drought stress tolerance via SA, JA and ET signaling (Cho et al., 2008).

Although recent studies have begun to reveal the molecular mechanisms of various plant responses to mVOCs, the mechanisms underlying many known phenotypic responses remain unclear. In this study, we aimed to determine how plants respond at a molecular level after exposure to different mVOCs. To accomplish this goal, we measured transcriptomic changes in Arabidopsis after PGPV and PGIV exposure. Using hierarchical clustering analysis, we found that PGPVs upregulated genes associated with growth and development but suppressed expressions of defense-associated genes. In contrast, PGIVs downregulated the expression of genes related to growth and development but induced the expression of genes associated with defense. In addition, our data suggested that WRKYs might serve as convergence nodes in SA-JA-mediated defense responses and growth-defense trade-offs. Importantly, our results revealed that PGPVs can promote plant growth by stimulating molecular pathways that correspond to different growth-associated processes.

2 MATERIALS AND METHODS

2.1 Plant growth condition and mVOCs exposure experiment

Seeds of A. thaliana ecotype Columbia-0 (Col-0) were surface sterilized by 1.6% NaClO (w/v) containing 0.067% tween20 (v/v) for 8 mins. After sterilization, the seeds were rinsed in sterile distilled water five times and vernalized for two days at 4°C in the dark. After vernalization, A. thaliana seeds were grown for five to six days on 1/4 Murashige and Skoog (MS) medium with 0.01 g ml⁻¹ sucrose (pH 5.7) in a 22°C growth chamber with a 16/8-h light/dark photoperiod.

The mVOCs exposure experiments were conducted as described previously (Chang et al., 2023). One 9-cm Petri dish with 2-days-old F. verticillioides and six 5.5-cm Petri dishes with 2-days-old S. sympodiophorum were used for the mVOC exposure experiments. TSB and PDA medium without microbes were used as the control treatment for mVOCs exposure experiments. During the mVOC exposure experiment, MS medium containing five to six-day-old Arabidopsis seedlings and TSB or PDA medium containing microbes were transferred individually to the sealed co-culture container and cultured in a 22°C growth chamber with a 16-h photoperiod, avoiding direct contact between the seedlings and microbes. For phenotype observation, Arabidopsis seedlings were treated with F. verticillioides mVOCs and S. sympodiophorum mVOCs for four and six days respectively. For transcriptomic analysis, Arabidopsis seedlings were treated with mVOCs for 24 hr. and then used for RNA extraction.

2.2 Microbial Identification and Cultural Condition

F. verticillioides (BCRC number FU31352) is an endophytic fungi in Miscanthus floridulus and was isolated from the seeds of M. floridulus. S. sympodiophorum (BCRC number FU31351) is an environmental fungus. A Forward primer ITS1 (5’-TCCGTAGGTGAACCTGCGG-3′) and reverse primer ITS4 (5’-TCCTCCGCTTATTGATATGC-3′) were used for PCR-based species identification. Amplified fragments were identified as F. verticillioides or S. sympodiophorum according to NCBI BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the sequenced PCR product. A pphylogenetic analysis of ITS fragments of Fusarium spp. and Simplicillium spp. was performed to confirm the result (Figure S1). The sequences of the ITS fragments of Fusarium spp. and Simplicillium spp. were obtained from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/). A phylogenetic tree was constructed in MEGA 11 with neighbor joining method.

A 0.9-cm diameter agar plaque containing F. verticillioides was cultured in a 9-cm Petri dish containing 20 mL TSB medium. The dish was maintained in a 28°C growth chamber for two days in the dark. S. sympodiophorum was grown on PDA medium, and spores were collected with 10% glycerol. The spores were diluted to 12 000 spores μl⁻¹ and stored at −80°C until further use. A volume of 2 μL of the spore suspension was cultured on 5.5-cm Petri dish containing 10 mL PDA medium and maintained at 28°C in a growth chamber for two days in dark.

2.3 Volatile compound analysis of PGIVs and PGPVs

Methods of volatile compound analysis are described in Chen et al., (2023). For sample preparation, F. verticillioides was grown on TSB medium for two days. S. sympodiophorum was grown on PDA medium for two days. E. aerogenes was grown on LB medium for one day. The samples were used for volatile collection. TSB, PDA and LB medium were used as controls.

2.4 RNA extraction and library construction

Total RNA was extracted from Arabidopsis seedlings with or without F. verticillioides and S. sympodiophorum mVOCs exposure using an RNeasy Plant Mini kit (Qiagen, Hilden, German). DNase I recombinant (Roche) was used for DNA degradation. RNeasy MInElute Cleanup kit (Qiagen) was used for total RNA purification. A NanoDrop™ 2000c Spectrophotometers (Thermo Scientific) was used for RNA Quantitation.

Total RNA from Arabidopsis seedlings with or without F. verticillioides and S. sympodiophorum mVOCs treatment was used for RNA-seq library construction. Two biological replicates were included for each treatment. Construction of RNA-seq libraries was performed according to the instructions provided with the Illumina NovaSeq platform, which generates 150-bp paired-end reads. Illumina sequencing was performed by Genewiz, Inc.

2.5 RNA-seq analysis

Raw data for Enterobacter aerogenes mVOCs-treated Arabidopsis was obtained from a previous study (Chang et al., 2023). Adaptor sequences and poor-quality bases were removed by Trimmomatic v0.36 (Bolger et al., 2014); a quality score threshold was set to 30. Transcript abundance was quantified by Kallisto (Trapnell et al., 2012). DESeq2 was used for differentially expressed gene identification (Love et al., 2014). Genes with | log2(FC) | ≥ 1, p-adj <0.9 were considered responsive genes. Genes identified as either PGPVs- or PGIVs-responsive genes were defined as mVOCs-responsive genes. Z-score based on transcript per million (TPM) values were used for Euclidean distance-based cluster analysis. Gene Ontology enrichment (GO) analysis was performed with BiNGO v3.0.3 (Maere et al., 2005). The significance of enriched categories was analyzed using a Hypergeometric test. The false discovery rate was determined by the Benjamini & Hochberg method (Benjamini and Yekutieli, 2001). TF enrichment analysis was performed by GenFam (Bedre and Mandadi, 2019). The significance of TF enrichment analysis results was tested with Fisher's exact test. HRPE motif analysis of the promoter region was performed in PlantPan 4.0 with the sequence ranging from 1000 bp upstream to 500 bp downstream of the transcription start site (Chow et al., 2019).

2.6 Microarray analysis

The transcriptomic data of Arabidopsis seedlings treated with PGIVs Serratia plymuthica HRO-C48 mVOCs were utilized to validate the findings in this study. The microarray data in Wenke et al., (2012) were analyzed to extract the transcriptomic changes induced by Serratia plymuthica HRO-C48 mVOCs in Arabidopsis seedlings. The Affymetrix CEL files of Serratia plymuthica HRO-C48 mVOCs-induced transcriptome data were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus with the accession number GSE35325 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35325). Normalization and differential expressed gene analysis were performed on transcriptomic data from Arabidopsis exposed to S. plymuthica HRO-C48 mVOCs for 24 hr. using the R package ‘limma’ and ‘oligo’ (Carvalho and Irizarry, 2010; Ritchie et al., 2015). Genes with | log2(FC) | ≥ 1, p-adj <0.9 were considered to be S. plymuthica HRO-C48 mVOCs-regulated genes.

2.7 Quantitative real-time PCR for relative gene expression analysis

For each sample, 1000 ng RNA was used for cDNA preparation by ImProm-II™ Reverse Transcriptase (Promega). RT-qPCR was performed using the GoTaq®qPCR Master Mix (Promega) on StepOnePlus Real-Time PCR Systems (Applied Biosystems). ACT8 was used as the reference gene. A list of primer sequences used for RT-qPCR is provided in Supplementary Table 1. Relative gene expression was calculated with the 2^−ΔΔCT method (Schmittgen and Livak, 2008).

2.8 Statistical analysis

Statistically significant differences in plant phenotypes and RT-qPCR results were assessed with Student's t-test. p < 0.05 was defined as statistically significant.

3 RESULTS

3.1 F. verticillioides mVOCs and S. sympodiophorum mVOCs promote plant growth

To examine whether F. verticillioides mVOCs and S. sympodiophorum mVOCs promote Arabidopsis growth, we grew Arabidopsis seedlings in the presence of F. verticillioides or S. sympodiophorum mVOCs. The Arabidopsis plants and microbes were grown on separate Petri dishes in a sealed environment, and Arabidopsis growth was monitored. Promotion of whole seedling growth was observed in Arabidopsis after exposure to F. verticillioides mVOCs for four days (Figure 1A, C-D). In the seedlings with S. sympodiophorum mVOCs exposure, significant promotion of the leaf surface area was observed after six days (Figure 1B, C-D). However, there was no observed promotion of root growth in S. sympodiophorum mVOCs-exposed plants (Figure 1B). These results indicate that both F. verticillioides and S. sympodiophorum mVOCs are PGPVs, though the induced growth phenotypes differ.

Details are in the caption following the image — **FIGURE 1**
Open in figure viewer PowerPoint

F. *verticillioides* and S. *sympodiophorum* mVOCs promote different Arabidopsis growth phenotypes and molecular responses. (A) Phenotype and the fresh weight of Arabidopsis seedlings after F. *verticillioides* mVOCs exposure for four days. The result is representative of three biological replicates. Scale bar, 1 cm. Each point represents one individual seedling. (B) Phenotype and fresh weight of Arabidopsis seedlings after S. *sympodiophorum* mVOCs exposure for six days. The result is representative of three biological replicates. Scale bar, 1 cm. Each point represents one individual seedlings. (C) Phenotype of Arabidopsis leaf surface area after F. *verticillioides* and S. *sympodiophorum* mVOCs exposure for four days and six days, respectively. (D) Measured leaf surface area in Arabidopsis after F. *verticillioides* and S. *sympodiophorum* mVOCs exposure for 4 and 6 days, respectively. FvVCs indicates *F. verticillioides* mVOCs and SsVCs indicates *S. sympodiophorum* mVOCs. Each dot point represents one individual seedling. Data is representative of three biological replicates. Similar results were observed in other individual experiments. Student's t-test was used for comparison. Asterisks indicate significant difference between control and mVOC treatment. *, p < 0.05; **, p < 0.01. The number of upregulated (E) and downregulated (F) genes for PGPVs and PGIV. Comparison of PGPV- and PGIV-upregulated (G) and downregulated (H) genes.

3.2 Identification of PGPVs and PGIVs

Although the phenotypic effects of different PGPVs and PGIVs have been well documented, the underlying regulatory mechanisms induced by PGPVs and PGIVs are still largely unknown (Cappellari and Banchio, 2020; Chang et al., 2023; Chiang et al., 2023; Pescador et al., 2022; Schulz-Bohm et al., 2017; Sharifi et al., 2022; Yasmin et al., 2021; Zamioudis et al., 2015). Since the effects of E. aerogenes mVOCs on plants growth and stress resilience have been well characterized (Chang et al., 2023; D'Alessandro et al., 2014), E. aerogenes mVOCs were used as a PGIV in this study. To investigate the chemical diversity of the tested PGPVs and PGIVs, GC–MS analysis was performed on the two PGPVs (F. verticillioides mVOCs and S. sympodiophorum mVOCs) and one PGIVs (E. aerogenes mVOCs). In the E. aerogenes mVOC samples, 3-methyl-1-Butanol, 2-Phenylethanol were identified as the key bioactive candidates. In the F. verticillioides mVOCs, dimethyl disulfide, 2,3-dimethyl-pyrazine and 1-ethyl-4-methoxy-benzene were identified as bioactive candidates. In the S. sympodiophorum mVOCs, acetoin, 3-methyl-1-butanol and phenylethyl alcohol were identified as bioactive candidates (Table 1).

TABLE 1. Volatiles emitted by E. aerogenes, F. verticillioides and S. sympodiophorum.

Emitting microbe	Compound name
E. aerogenes, S. sympodiophorum	1-Butanol, 3-methyl-
E. aerogenes	2-Phenylethanol
F. verticillioides	Disulfide, dimethyl
F. verticillioides	Pyrazine, 2,3-dimethyl-
F. verticillioides	Benzene, 1-ethyl-4-methoxy-
S. sympodiophorum	Acetoin
S. sympodiophorum	Phenylethyl Alcohol

3.3 PGPVs- and PGIVs-induced transcriptional changes in Arabidopsis thaliana

To understand the mechanisms of the Arabidopsis response to different mVOCs, the mVOCs-responsive genes were probed by conducting RNA-seq analysis of Arabidopsis plants exposed to two PGPVs (F. verticillioides mVOCs and S. sympodiophorum mVOCs) and one PGIVs (E. aerogenes mVOCs). A total of 123 genes were identified as F. verticillioides mVOC-responsive genes (Figure 1E, F). Among these genes, 48 were upregulated and 75 were downregulated. Furthermore, a total of 63 Arabidopsis genes were identified as S. sympodiophorum mVOC-responsive genes. Among the S. sympodiophorum mVOC-responsive genes, 35 were upregulated, and 28 were downregulated (Figure 1E, F). A total of 3813 genes were identified as E. aerogenes mVOC-responsive genes. Among these responsive genes, 2110 were upregulated, and 1703 were downregulated (Figure 1E, F). Comparing the list of F. verticillioides mVOC- and S. sympodiophorum mVOC-responsive genes revealed that only a small proportion were commonly upregulated (18.6%) or downregulated (10.8%) by both types of mVOCs (Figure 1G, H).

GO analysis revealed that genes related to anaerobic respiration were commonly upregulated in response to the PGPVs and PGIV (Figure 2A, B, S3). Futhermore, genes upregulated by F. verticillioides mVOCs were enriched in functions associated with post-transcriptional regulation and centromere complex assembly. For genes upregulated by S. sympodiophorum mVOCs, no significant GO terms were identified. Meanwhile, the PGIV-upregulated genes were commonly associated with defense and immunity (Figure 2A). No significant GO terms were identified for genes downregulated by either F. verticillioides mVOCs or S. sympodiophorum mVOCs. Genes with functions related to development and stress response were enriched in the E. aerogenes mVOC-downregulated gene list (Figure 2B). To determine if any general regulatory mechanisms were transcriptionally affected by PGPVs, we examined the functions of the 13 common PGPV-upregulated genes and 10 common PGPV-downregulated genes. Intriguingly, core hypoxia-responsive genes (HRGs) accounted for 84.6% of the common PGPV-upregulated genes (11 out of 13; Mustroph et al., 2009; Table S2). In previous work, the hypoxia-responsive promoter element (HRPE) was identified as a key regulatory motif (interacts with ERF VII) in about half of the 49 core hypoxia-responsive genes (Gasch et al., 2016; Mustroph et al., 2009). Thus, we next checked if the two PGPV-upregulated genes associated with hypoxia response might contain HPREs in their promoter regions (Table S2). However, the two non-hypoxia-associated PGPV-upregulated genes do not contain the HEPE motif, suggesting there may be an alternative regulatory mechanism that upregulates gene expression in response to PGPV exposure.

TFs are key regulators of transcriptional responses in plants and other organisms. To explore potential regulatory components that may contribute to phenotypes of growth promotion or inhibition, TF enrichment analysis was performed. The results showed that the FAR1 and LBD families were respectively enriched in the gene sets upregulated by F. verticillioides and S. sympodiophorum mVOCs (Figure 3A, Table S3-S6). In the set of PGIV (E. aerogenes mVOC)-upregulated genes, HSFs, NACs and WRKYs (modulate stress resilience) were enriched (Figure 3A, Table S3-S6). Intriguingly, WRKYs were also enriched in F. verticillioides mVOC-downregulated genes, while bHLHs were enriched in E. aerogenes mVOC-downregulated genes (Figure 3B, Table S3-S6).

3.4 Hierarchical clustering reveals transcriptomic features of Arabidopsis response to PGPVs and PGIVs

To explore the difference in Arabidopsis transcriptomic responses to different mVOCs, we performed hierarchical clustering of mVOCs-responsive genes. The mVOCs-responsive genes were sorted into 10 clusters (c1-c10) using R package pheatmap with default parameter settings. The analysis results showed that c1, c2, c7 and c9 were induced by the PGIVs, whereas c3, c5, c6 and c8 were suppressed by the PGIVs (Figure 4). GO enrichment analysis showed that PGIV-induced clusters (c2) were enriched in functions related to immune response, abscisic acid (ABA) response, JA signaling, SA signaling and ET biosynthesis. Functions of genes in the PGIV-suppressed cluster were enriched in cell wall organization and root morphogenesis (Figure 4). Genes related to immunity were enriched in c4, including MYB51, WRKY33, FRK1, CBP60g, SARD1 (Figure 4). No identified clusters were induced or suppressed by PGPVs (Figure 4).

Interestingly, expression levels of c3 and c5 genes were consistently higher after PGPV treatment, as compared with the PGIVs-treated samples. In contrast, c2 genes were upregulated by PGIV exposure, but the transcript levels were lower in the presence of PGPVs. GO term analysis also revealed that c3 is enriched in genes with functions related to cell wall organization and development. Meanwhile, c5 is enriched in genes with functions related to nicotianamine biosynthesis (Figure 4). By comparing the gene expression pattern between PGPV-exposed plants and PGIV-exposed plants, the result suggest that genes associated with growth and development have higher expressions after PGPV treatment. Moreover, lower expression of genes associated with defense and immunity was observed after PGPV exposure, as compared to PGIV exposure (Figure 4). To extend this idea, we examined the previously reported transcriptomic changes induced by S. plymuthica HRO-C48 mVOCs in Arabidopsis. S. plymuthica HRO-C48 is a rhizobacteria (Kalbe et al., 1996), as its mVOCs inhibit Arabidopsis leaf and root growth after 24 h of exposure (Wenke et al., 2012). GO analysis indicates that S. plymuthica HRO-C48 mVOC-upregulated genes are enriched with in functions associated with defense and immunity response. Meanwhile, S. plymuthica HRO-C48 mVOC-downregulated genes are enriched with functions associated with stress response and auxin response (Figure S2).

TF enrichment analysis on gene clusters further showed that MYBs and bHLHs were enriched in c1. In c2, enriched TF families included HSFs, NACs and WRKYs, which modulate plant stress resilience. Finally, HSFs and CAMTAs were enriched in c7 (Figure 5).

3.5 Plant immune systems respond to diverse mVOCs in different ways

In a previous study, we found that mVOCs prime plant disease resistance (Chang et al., 2023; Chiang et al., 2023). Therefore, we wondered whether mVOCs might increase the expression of genes associated with plant disease resistance and thereby improve stress resilience. In plants, the immune systems can be subdivided into two categories, induced systemic resistance (ISR) and systemic acquired resistance (SAR). These two types of responses are respectively induced by beneficial microbes and pathogens (Vlot et al., 2021). To clarify which type of response is induced by the tested mVOCs, we examined ISR- and SAR-associated gene expression profiles in Arabidopsis after PGPVs and PGIVs exposure. In ISR responses, MYC2 typically serves as a central regulator. For instance, MYC2 negatively regulates camalexin biosynthesis genes in the context of JA-mediated defense responses (Millet et al., 2010). Our data further showed that the level of MYC2 was lower after E. aerogenes mVOCs treatment than after F. verticillioides and S. sympodiophorum mVOC treatments. In contrast, the levels of camalexin biosynthesis gene PAD3 were higher after E. aerogenes mVOC treatment than after F. verticillioides and S. sympodiophorum mVOC treatments (Figure 6A). It is known that SARD1 is a key TF that drives SA-mediated SAR responses. Thus, we next investigate the SARD1-targeting gene expression profiles (Sun et al., 2015). The result demonstrates that F. verticillioides and S. sympodiophorum mVOCs respectively upregulated 3.45% and 10.34% of SARD1-targeting genes. However, 55.17% of SARD1-targeting genes were upregulated by E. aerogenes mVOCs. These results suggest that ISR responses are induced by both F. verticillioides and S. sympodiophorum mVOCs. Meanwhile, SAR response plays a central role in the effects of E. aerogenes mVOCs (Figure 6B).

4 DISCUSSION

In this study, we showed that mVOCs of both F. verticillioides and S. sympodiophorum increase plant growth by affecting different organs in the seedlings. mVOCs of F. verticillioides promoted Arabidopsis growth of whole seedlings, while mVOCs of S. sympodiophorum only increased the leaf surface area (Figure 1A-D). Due to the diversity of the microbial metabolic profiles among different species, the chemical components of mVOCs emitted by each species can be extremely varied. Thus, our results imply that the different phenotypes in Arabidopsis after exposure to different mVOCs could be due to the different constituent compounds in F. verticillioides and S. sympodiophorum mVOCs.

In addition, the comparison of F. verticillioides mVOC- and S. sympodiophorum mVOC-responsive genes showed that less than 40% of upregulated and downregulated genes were commonly regulated by both PGPVs. The result implies that Arabidopsis might respond to F. verticillioides mVOCs and S. sympodiophorum mVOCs in a different manner at the molecular level (Figure 1G, H). In support of this idea, TF enrichment analysis of mVOC-responsive genes showed that the FAR1 family is enriched in the F. verticillioides mVOC-upregulated genes, while LBD family is enriched in the S. sympodiophorum mVOC-upregulated genes (Figure 3). Both the FAR1 and LBD families are known to mediate plant growth and development. FAR1 family members are involved in the light-mediated regulation of plant development (Lin and Wang, 2004), while LBDs are essential to the regulation of leaf size and shape (Shuai et al., 2002). Thus, the results suggest that F. verticillioides mVOCs might increase the size of whole Arabidopsis seedlings by stimulating FAR1 activity. However, S. sympodiophorum mVOCs affect LBDs to increase the leaf surface area in Arabidopsis.

Interestingly, we found that WRKYs were enriched in F. verticillioides mVOCs downregulated genes (Figure 3). WRKYs constitute a large family of TFs and have diverse roles in growth, development and stress responses in plants (F. Chen et al., 2017; J. Chen et al., 2017; Chi et al., 2013). Thus, we closely examined the list of identified WRKY TFs enriched in F. verticillioides mVOC-downregulated genes. The results indicated that WRKY38, WRKY51, WRKY54 and WRKY62 were downregulated by F. verticillioides mVOCs. Each of these TFs is associated with defense and immunity regulation (Table S4). WRKY51 is known to be an activator of SA-mediating responses and a repressor of JA-mediating responses (Gao et al., 2011). Additionally, both WRKY38 and WRKY62 are the downstream regulators of SA signaling (Xie et al., 2010). WRKY62 is a repressor of JA-mediating responses (Mao et al., 2007). WRKY54 is a homolog of WRKY70 (Wang et al., 2006), and both of these proteins act as negative regulators of cell wall-associated defenses against necrotrophic pathogens (Li et al., 2017). In addition, WRKY70 serves as a central node for activation of SA-mediating defense responses, but it represses JA-mediating defense responses (Li et al., 2006, 2004). Collectively, our expression data and these known functions of different WRKYs suggests that F. verticillioides mVOCs might repress SA-mediated defense responses and promote JA-mediated defense responses. In line with this idea, we also found that the central regulator of JA signaling, MYC2 was upregulated by F. verticillioides mVOCs.

SA-mediated signaling and JA/ET-mediated signaling respectively constitute crucial regulatory mechanisms in plant defense against hemibiotrophic/biotrophic pathogens and necrotrophic pathogens (Glazebrook, 2005). F. verticillioides (formerly F. moniliforme) was reported to grow within its host either as a hemibiotrophic pathogen or as an endophyte (Lanubile et al., 2014; Lee et al., 2009; Murillo-Williams and Munkvold, 2008), wth environmental factors modulating its pathogenicity (Murillo-Williams and Munkvold, 2008). In light of our findings, it can be speculated that mVOCs of hemibiotroph F. verticillioides serve as signals to weaken plant resistance via the antagonistic interplay between SA and JA signaling pathways.

Growth-defense trade-offs are important strategies utilized by plants to reallocate resources and prioritize either growth or defense. However, plants exposed to mVOCs can often mount both growth promotion and defense responses at the same time (Blom et al., 2011; Fukada, 2024; Zamioudis et al., 2015, 2013). It was previously shown that E. aerogenes mVOCs are PGIVs, which inhibit plant growth and prime plant disease resistance (Chang et al., 2023). In contrast, we found that F. verticillioides and S. sympodiophorum mVOCs are PGPVs that promote plant growth. In this study, we identified a total of 2682 genes that respond to these three types of mVOCs. According to our hierarchical clustering analysis, the mVOC-responsive genes were classified into 10 clusters based on their expression pattern in response to PGPVs and PGIVs. The largest clusters were c2 and c3. These two clusters exhibited opposite patterns. Genes in c2 are mainly associated with defense and immunity, while genes in c3 are mostly involved in growth and development (Figure 4). Our data demonstrated that genes in c3 exhibited higher mRNA levels in Arabidopsis exposed to PGPVs from F. verticillioides and S. sympodiophorum (Figure 4). In contrast, genes in c2 exhibited low mRNA levels after exposure to PGPVs from F. verticillioides and S. sympodiophorum (Figure 4). Conversely, PGIVs from E. aerogenes largely induced c2 genes and suppressed c3 genes (Figure 4). Collectively, the results show that PGIVs-induced Arabidopsis growth inhibition corresponds to the upregulation of a large set of defense-associated genes. He et al., (2022) proposed that the reallocation of energy in growth-defense trade-offs may be modulated by the intensity of environmental stimuli, suggesting that growth-defense trade-off is not an all-or-nothing type of response. This idea could help to explain why plants may simultaneously exhibit both growth promotion and defense reinforcement under certain conditions of mVOC exposure.

Our findings provide insights into how plants may partition energy resources into growth and defense responses at a molecular level. Comparing the transcriptional changes in PGPVs- and PGIVs-treated plants, we found that the numerous defense-associated genes were upregulated in the transcriptome of PGIVs-treated plants but PGPV-treated plants (Figure 4). It is known that WRKYs play essential roles in diverse stress-induced responses in plants (Chen et al., 2019; Goyal et al., 2023). Furthermore, WRKY18 is inducible after PGIVs treatment and served as a negative regulator of plant growth (Wenke et al., 2012). Our results (Figure 3 and Table S3-S6) demonstrated that WRKYs were enriched among PGPV-downregulated genes and in PGIV-upregulated genes. Overall, the changes in transcript levels of WRKYs seemed to exhibit a similar pattern with the number of mVOCs-induced defense-associated genes. Thus, we postulate that WRKYs might serve as convergence nodes to integrate the intensity, frequency and duration of environmental stress cues. Transcriptional regulation of WRKYs may allow the plants to direct energy into required biological processes, underlying the growth-defense trade-off.

To further explore this idea, we considered stress resilience in Arabidopsis after different PGPV and PGIV exposures. Copper-based antimicrobial compounds (CBACs) have been used as agricultural protectants against pathogens for over a hundred years (Lamichhane et al., 2018), and it is known that copper enhances plant disease resistance by suppressing ABA biosynthesis (Liu et al., 2020). However, suppressing ABA biosynthesis can impair stomatal closure and thus lead to a higher transpiration rate (Liu et al., 2020), making plants more susceptible to drought stress. In our previously published study, we found that mVOCs of Tolypocladium inflatum GT22 promote plant growth and improve both copper stress tolerance and disease resistance (Chiang et al., 2023). Thus, we examined the expression of factors involved in resistance to copper stress in our data. Our results suggested that copper stress tolerance was not improved in Arabidopsis after F. verticillioides and S. sympodiophorum mVOC treatments (Figure S4). However, Arabidopsis treated with E. aerogenes mVOCs exhibited improved copper tolerance (Figure S4). Combining these findings with our previous result in Chiang et al., (2023), we conclude that the number of upregulated WRKYs is well-aligned with stress resilience and growth retardation in plants (Table S7).

The microbiomes can provide a host plant with various advantages, including growth promotion, nutrient uptake and stress resilience (Trivedi et al., 2020). Plant-associated microbiomes do not just exist in modern land plants. In fact, the ancestor of land plants, green algae (Tena, 2020), formed symbiotic relationships with microbes in order to obtain vitamin B₁₂ (Croft et al., 2005). In addition, the earliest land plant, Marchantia (Bowman et al., 2017), had a microbiome, although its functions are still unclear (Alcaraz et al., 2018; Marks et al., 2018; Wicaksono et al., 2023). Since mVOCs are gaseous metabolites emitted by microbes, it is plausible that plants have evolved different strategies to respond to signals from different microbes. In this study, we found that HSFs, NACs and WRKYs are the TF families that are enriched in the defense-associated cluster of mVOC-regulated genes (c2, Figure 5). HSFs, NACs and WRKYs also existed in the early-diverging Marchantia and are crucial in both defense and development in modern land plants (Eulgem et al., 2000; Jin et al., 2014; Scharf et al., 2012; Wani et al., 2021; Yuan et al., 2019). Thus, our results imply that HSFs, NACs and WRKYs might be essential regulators of mVOCs-induced responses and exhibit conserved functions in various land plants exposed to mVOCs.

AUTHOR CONTRIBUTIONS

C.H.C. analyzed the data and wrote the manuscript. C.H.C., C.C.H., P.Y.S., Y.S.C. and C.Y.W. performed the experiments. H.M.T. and Y.Y.Z. performed the volatile compound analysis. I.J.L. and C.C.H. performed the microbe isolation and microbial identification. H.J.H. organized this work.

ACKNOWLEDGMENTS

We would like to thank National Science and Technology Council (NSTC) for offering funding to this project. We thank Technology Commons (TechComm), College of Life Science, National Taiwan University, for the VOCs trapping and GC–MS system.

FUNDING INFORMATION

This work was supported by National Science and Technology Council (NSTC-112-2313-B-006 -005-, MOST-111-2313-B-006-005-, MOST-109-2313-B-006-008-).

Open Research

DATA AVAILABILITY STATEMENT

The data underlying this article are provided as supplemental tables.

Supporting Information

REFERENCES

Alcaraz, L.D., Peimbert, M., Barajas, H.R., Dorantes-Acosta, A.E., Bowman, J.L., Arteaga-Vázquez, M.A., 2018. Marchantia liverworts as a proxy to plants' basal microbiomes. Sci Rep 8, 12712. https://doi.org/10.1038/s41598-018-31168-0
10.1038/s41598-018-31168-0
PubMed Web of Science® Google Scholar
Bedre, R., Mandadi, K., 2019. GenFam: A web application and database for gene family-based classification and functional enrichment analysis. Plant Direct 3, e00191. https://doi.org/10.1002/pld3.191
10.1002/pld3.191
PubMed Web of Science® Google Scholar
Benjamini, Y., Yekutieli, D., 2001. The Control of the False Discovery Rate in Multiple Testing under Dependency. The Annals of Statistics 29, 1165–1188.
10.1214/aos/1013699998
Web of Science® Google Scholar
Bhattacharyya, D., Garladinne, M., Lee, Y.H., 2015. Volatile Indole Produced by Rhizobacterium Proteus vulgaris JBLS202 Stimulates Growth of Arabidopsis thaliana Through Auxin, Cytokinin, and Brassinosteroid Pathways. J Plant Growth Regul 34, 158–168. https://doi.org/10.1007/s00344-014-9453-x
10.1007/s00344-014-9453-x
CAS Web of Science® Google Scholar
Blom, D., Fabbri, C., Connor, E.C., Schiestl, F.P., Klauser, D.R., Boller, T., Eberl, L., Weisskopf, L., 2011. Production of plant growth modulating volatiles is widespread among rhizosphere bacteria and strongly depends on culture conditions. Environmental Microbiology 13, 3047–3058. https://doi.org/10.1111/j.1462-2920.2011.02582.x
10.1111/j.1462-2920.2011.02582.x
CAS PubMed Web of Science® Google Scholar
Bowman, J.L., Kohchi, T., Yamato, K.T., Jenkins, J., Shu, S., Ishizaki, K., Yamaoka, S., Nishihama, R., Nakamura, Y., Berger, F., Adam, C., Aki, S.S., Althoff, F., Araki, T., Arteaga-Vazquez, M.A., Balasubrmanian, S., Barry, K., Bauer, D., Boehm, C.R., Briginshaw, L., Caballero-Perez, J., Catarino, B., Chen, F., Chiyoda, S., Chovatia, M., Davies, K.M., Delmans, M., Demura, T., Dierschke, T., Dolan, L., Dorantes-Acosta, A.E., Eklund, D.M., Florent, S.N., Flores-Sandoval, E., Fujiyama, A., Fukuzawa, H., Galik, B., Grimanelli, D., Grimwood, J., Grossniklaus, U., Hamada, T., Haseloff, J., Hetherington, A.J., Higo, A., Hirakawa, Y., Hundley, H.N., Ikeda, Y., Inoue, K., Inoue, S., Ishida, S., Jia, Q., Kakita, M., Kanazawa, T., Kawai, Y., Kawashima, T., Kennedy, M., Kinose, K., Kinoshita, T., Kohara, Y., Koide, E., Komatsu, K., Kopischke, S., Kubo, M., Kyozuka, J., Lagercrantz, U., Lin, S.-S., Lindquist, E., Lipzen, A.M., Lu, C.-W., De Luna, E., Martienssen, R.A., Minamino, N., Mizutani, Masaharu, Mizutani, Miya, Mochizuki, N., Monte, I., Mosher, R., Nagasaki, H., Nakagami, H., Naramoto, S., Nishitani, K., Ohtani, M., Okamoto, T., Okumura, M., Phillips, J., Pollak, B., Reinders, A., Rövekamp, M., Sano, R., Sawa, S., Schmid, M.W., Shirakawa, M., Solano, R., Spunde, A., Suetsugu, N., Sugano, S., Sugiyama, A., Sun, R., Suzuki, Y., Takenaka, M., Takezawa, D., Tomogane, H., Tsuzuki, M., Ueda, T., Umeda, M., Ward, J.M., Watanabe, Y., Yazaki, K., Yokoyama, R., Yoshitake, Y., Yotsui, I., Zachgo, S., Schmutz, J., 2017. Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome. Cell 171, 287-304.e15. https://doi.org/10.1016/j.cell.2017.09.030
10.1016/j.cell.2017.09.030
CAS PubMed Web of Science® Google Scholar
Cappellari, L.D.R., Banchio, E., 2020. Microbial Volatile Organic Compounds Produced by Bacillus amyloliquefaciens GB03 Ameliorate the Effects of Salt Stress in Mentha piperita Principally Through Acetoin Emission. J Plant Growth Regul 39, 764–775. https://doi.org/10.1007/s00344-019-10020-3
10.1007/s00344-019-10020-3
CAS Web of Science® Google Scholar
Carvalho, B.S., Irizarry, R.A., 2010. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367. https://doi.org/10.1093/bioinformatics/btq431
10.1093/bioinformatics/btq431
CAS PubMed Web of Science® Google Scholar
Castulo-Rubio, D.Y., Alejandre-Ramírez, N.A., Orozco-Mosqueda, M.D.C., Santoyo, G., Macías-Rodríguez, L.I., Valencia-Cantero, E., 2015. Volatile Organic Compounds Produced by the Rhizobacterium Arthrobacter agilis UMCV2 Modulate Sorghum bicolor (Strategy II Plant) Morphogenesis and SbFRO1 Transcription In Vitro. J Plant Growth Regul 34, 611–623. https://doi.org/10.1007/s00344-015-9495-8
10.1007/s00344-015-9495-8
CAS Web of Science® Google Scholar
Chang, C.-H., Wang, W.-G., Su, P.-Y., Chen, Y.-S., Nguyen, T.-P., Xu, J., Ohme-Takagi, M., Mimura, T., Hou, P.-F., Huang, H.-J., 2023. The involvement of AtMKK1 and AtMKK3 in plant-deleterious microbial volatile compounds-induced defense responses. Plant Mol Biol 111, 21–36. https://doi.org/10.1007/s11103-022-01308-2
10.1007/s11103-022-01308-2
CAS PubMed Google Scholar
Chen, F., Hu, Y., Vannozzi, A., Wu, K., Cai, H., Qin, Y., Mullis, A., Lin, Z., Zhang, L., 2017. The WRKY Transcription Factor Family in Model Plants and Crops. Critical Reviews in Plant Sciences 36, 311–335. https://doi.org/10.1080/07352689.2018.1441103
10.1080/07352689.2018.1441103
Web of Science® Google Scholar
Chen, J., Nolan, T., Ye, H., Zhang, M., Tong, H., Xin, P., Chu, J., Chu, C., Li, Z., Yin, Y., 2017. Arabidopsis WRKY46, WRKY54 and WRKY70 Transcription Factors Are Involved in Brassinosteroid-Regulated Plant Growth and Drought Response. Plant Cell tpc.00364.2017. https://doi.org/10.1105/tpc.17.00364
10.1105/tpc.17.00364
Google Scholar
Chen, X., Li, C., Wang, H., Guo, Z., 2019. WRKY transcription factors: evolution, binding, and action. Phytopathol Res 1, 13. https://doi.org/10.1186/s42483-019-0022-x
10.1186/s42483-019-0022-x
Web of Science® Google Scholar
Chen, Y.-J., Cheah, B.H., Lin, C.-Y., Ku, Y.-T., Kuo, C.-H., Zhang, Y.-Y., Chen, B.-R., Nean, O., Hsieh, C.-H., Yeh, P.-M., Yeo, F.K.S., Lin, Y.-P., Chuang, W.-P., Lee, C.-R., Ting, H.-M., 2023. Inducible chemical defenses in wild mungbean confer resistance to Spodoptera litura and possibly at the expense of drought tolerance. Environmental and Experimental Botany 205, 105100. https://doi.org/10.1016/j.envexpbot.2022.105100
10.1016/j.envexpbot.2022.105100
CAS Web of Science® Google Scholar
Chi, Y., Yang, Y., Zhou, Y., Zhou, J., Fan, B., Yu, J.-Q., Chen, Z., 2013. Protein–Protein Interactions in the Regulation of WRKY Transcription Factors. Molecular Plant 6, 287–300. https://doi.org/10.1093/mp/sst026
10.1093/mp/sst026
CAS PubMed Web of Science® Google Scholar
Chiang, C.-Y., Chang, C.-H., Tseng, T.-Y., Nguyen, V.-A.T., Su, P.-Y., Truong, T.-T.T., Chen, J.-Y., Huang, C.-C., Huang, H.-J., 2023. Volatile Compounds Emitted by Plant Growth–Promoting Fungus Tolypocladium inflatum GT22 Alleviate Copper and Pathogen Stress. Plant And Cell Physiology pcad120. https://doi.org/10.1093/pcp/pcad120
10.1093/pcp/pcad120
Google Scholar
Cho, S.M., Kang, B.R., Han, S.H., Anderson, A.J., Park, J.-Y., Lee, Y.-H., Cho, B.H., Yang, K.-Y., Ryu, C.-M., Kim, Y.C., 2008. 2R,3R-Butanediol, a Bacterial Volatile Produced by Pseudomonas chlororaphis O6, Is Involved in Induction of Systemic Tolerance to Drought in Arabidopsis thaliana. MPMI 21, 1067–1075. https://doi.org/10.1094/MPMI-21-8-1067
10.1094/MPMI-21-8-1067
CAS PubMed Web of Science® Google Scholar
Chow, C.-N., Lee, T.-Y., Hung, Y.-C., Li, G.-Z., Tseng, K.-C., Liu, Y.-H., Kuo, P.-L., Zheng, H.-Q., Chang, W.-C., 2019. PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants. Nucleic Acids Research 47, D1155–D1163. https://doi.org/10.1093/nar/gky1081
10.1093/nar/gky1081
PubMed Web of Science® Google Scholar
Croft, M.T., Lawrence, A.D., Raux-Deery, E., Warren, M.J., Smith, A.G., 2005. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93. https://doi.org/10.1038/nature04056
10.1038/nature04056
CAS PubMed Web of Science® Google Scholar
D'Alessandro, M., Erb, M., Ton, J., Brandenburg, A., Karlen, D., Zopfi, J., Turlings, T.C., 2014. Volatiles produced by soil-borne endophytic bacteria increase plant pathogen resistance and affect tritrophic interactions. Plant, Cell & Environment 37, 813–826.
10.1111/pce.12220
CAS PubMed Web of Science® Google Scholar
De Zelicourt, A., Al-Yousif, M., Hirt, H., 2013. Rhizosphere Microbes as Essential Partners for Plant Stress Tolerance. Molecular Plant 6, 242–245. https://doi.org/10.1093/mp/sst028
10.1093/mp/sst028
CAS PubMed Web of Science® Google Scholar
Esparza-Reynoso, S., Ruíz-Herrera, L.F., Pelagio-Flores, R., Macías-Rodríguez, L.I., Martínez-Trujillo, M., López-Coria, M., Sánchez-Nieto, S., Herrera-Estrella, A., López-Bucio, J., 2021. Trichoderma atroviride -emitted volatiles improve growth of Arabidopsis seedlings through modulation of sucrose transport and metabolism. Plant Cell & Environment 44, 1961–1976. https://doi.org/10.1111/pce.14014
10.1111/pce.14014
CAS PubMed Web of Science® Google Scholar
Eulgem, T., Rushton, P.J., Robatzek, S., Somssich, I.E., 2000. The WRKY superfamily of plant transcription factors. Trends in Plant Science 5, 199–206. https://doi.org/10.1016/S1360-1385(00)01600-9
10.1016/S1360-1385(00)01600-9
CAS PubMed Web of Science® Google Scholar
Fukada, F., 2024. Mitigating the Trade-Off between Growth and Stress Resistance in Plants by Fungal Volatile Compounds. Plant And Cell Physiology 65, 175–178. https://doi.org/10.1093/pcp/pcae005
10.1093/pcp/pcae005
CAS PubMed Google Scholar
Gao, Q.-M., Venugopal, S., Navarre, D., Kachroo, A., 2011. Low Oleic Acid-Derived Repression of Jasmonic Acid-Inducible Defense Responses Requires the WRKY50 and WRKY51 Proteins. Plant Physiology 155, 464–476. https://doi.org/10.1104/pp.110.166876
10.1104/pp.110.166876
CAS PubMed Web of Science® Google Scholar
Gao, Y., Feng, J., Wu, J., Wang, K., Wu, S., Liu, H., Jiang, M., 2022. Transcriptome analysis of the growth-promoting effect of volatile organic compounds produced by Microbacterium aurantiacum GX14001 on tobacco (Nicotiana benthamiana). BMC Plant Biol 22, 208. https://doi.org/10.1186/s12870-022-03591-z
10.1186/s12870-022-03591-z
CAS PubMed Google Scholar
García-Gómez, P., Bahaji, A., Gámez-Arcas, S., Muñoz, F.J., Sánchez-López, Á.M., Almagro, G., Baroja-Fernández, E., Ameztoy, K., De Diego, N., Ugena, L., Spíchal, L., Doležal, K., Hajirezaei, M., Romero, L.C., García, I., Pozueta-Romero, J., 2020. Volatiles from the fungal phytopathogen Penicillium aurantiogriseum modulate root metabolism and architecture through proteome resetting. Plant Cell & Environment 43, 2551–2570. https://doi.org/10.1111/pce.13817
10.1111/pce.13817
CAS PubMed Web of Science® Google Scholar
Gasch, P., Fundinger, M., Müller, J.T., Lee, T., Bailey-Serres, J., Mustroph, A., 2016. Redundant ERF-VII Transcription Factors Bind to an Evolutionarily Conserved cis -Motif to Regulate Hypoxia-Responsive Gene Expression in Arabidopsis. Plant Cell 28, 160–180. https://doi.org/10.1105/tpc.15.00866
10.1105/tpc.15.00866
CAS PubMed Web of Science® Google Scholar
Glazebrook, J., 2005. Contrasting Mechanisms of Defense Against Biotrophic and Necrotrophic Pathogens. Annu. Rev. Phytopathol. 43, 205–227. https://doi.org/10.1146/annurev.phyto.43.040204.135923
10.1146/annurev.phyto.43.040204.135923
CAS PubMed Web of Science® Google Scholar
Goyal, P., Devi, R., Verma, B., Hussain, S., Arora, P., Tabassum, R., Gupta, S., 2023. WRKY transcription factors: evolution, regulation, and functional diversity in plants. Protoplasma 260, 331–348. https://doi.org/10.1007/s00709-022-01794-7
10.1007/s00709-022-01794-7
CAS PubMed Web of Science® Google Scholar
He, Z., Webster, S., He, S.Y., 2022. Growth–defense trade-offs in plants. Current Biology 32, R634–R639. https://doi.org/10.1016/j.cub.2022.04.070
10.1016/j.cub.2022.04.070
CAS PubMed Web of Science® Google Scholar
Jin, J., Zhang, H., Kong, L., Gao, G., Luo, J., 2014. PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucl. Acids Res. 42, D1182–D1187. https://doi.org/10.1093/nar/gkt1016
10.1093/nar/gkt1016
CAS PubMed Web of Science® Google Scholar
Kalbe, C., Marten, P., Berg, G., 1996. Members of the genus Serratia as beneficial rhizobacteria of oilseed rape. Microbiological Research 151, 4433--4400.
10.1016/S0944-5013(96)80014-0
Google Scholar
Kamaruzzaman, M., Wang, Z., Wu, M., Yang, L., Han, Y., Li, G., Zhang, J., 2021. Promotion of tomato growth by the volatiles produced by the hypovirulent strain QT5-19 of the plant gray mold fungus Botrytis cinerea. Microbiological Research 247, 126731. https://doi.org/10.1016/j.micres.2021.126731
10.1016/j.micres.2021.126731
CAS PubMed Web of Science® Google Scholar
Kanchiswamy, C.N., Malnoy, M., Maffei, M.E., 2015. Chemical diversity of microbial volatiles and their potential for plant growth and productivity. Front. Plant Sci. 6. https://doi.org/10.3389/fpls.2015.00151
10.3389/fpls.2015.00151
PubMed Google Scholar
Lamichhane, J.R., Osdaghi, E., Behlau, F., Köhl, J., Jones, J.B., Aubertot, J.-N., 2018. Thirteen decades of antimicrobial copper compounds applied in agriculture. A review. Agron. Sustain. Dev. 38, 28. https://doi.org/10.1007/s13593-018-0503-9
10.1007/s13593-018-0503-9
Web of Science® Google Scholar
Lanubile, A., Ferrarini, A., Maschietto, V., Delledonne, M., Marocco, A., Bellin, D., 2014. Functional genomic analysis of constitutive and inducible defense responses to Fusarium verticillioides infection in maize genotypes with contrasting ear rot resistance. BMC Genomics 15, 710. https://doi.org/10.1186/1471-2164-15-710
10.1186/1471-2164-15-710
PubMed Google Scholar
Lee, K., Pan, J.J., May, G., 2009. Endophytic Fusarium verticillioides reduces disease severity caused by Ustilago maydis on maize. FEMS Microbiology Letters 299, 31–37. https://doi.org/10.1111/j.1574-6968.2009.01719.x
10.1111/j.1574-6968.2009.01719.x
CAS PubMed Web of Science® Google Scholar
Li, J., Brader, G., Kariola, T., Tapio Palva, E., 2006. WRKY70 modulates the selection of signaling pathways in plant defense. The Plant Journal 46, 477–491. https://doi.org/10.1111/j.1365-313X.2006.02712.x
10.1111/j.1365-313X.2006.02712.x
CAS PubMed Web of Science® Google Scholar
Li, J., Brader, G., Palva, E.T., 2004. The WRKY70 Transcription Factor: A Node of Convergence for Jasmonate-Mediated and Salicylate-Mediated Signals in Plant Defense[W]. The Plant Cell 16, 319–331. https://doi.org/10.1105/tpc.016980
10.1105/tpc.016980
CAS PubMed Web of Science® Google Scholar
Li, J., Zhong, R., Palva, E.T., 2017. WRKY70 and its homolog WRKY54 negatively modulate the cell wall-associated defenses to necrotrophic pathogens in Arabidopsis. PLoS ONE 12, e0183731. https://doi.org/10.1371/journal.pone.0183731
10.1371/journal.pone.0183731
PubMed Web of Science® Google Scholar
Lin, R., Wang, H., 2004. Arabidopsis FHY3/FAR1 Gene Family and Distinct Roles of Its Members in Light Control of Arabidopsis Development. Plant Physiology 136, 4010–4022. https://doi.org/10.1104/pp.104.052191
10.1104/pp.104.052191
CAS PubMed Web of Science® Google Scholar
Liu, H., Xue, X., Yu, Y., Xu, M., Lu, C., Meng, X., Zhang, B., Ding, X., Chu, Z., 2020. Copper ions suppress abscisic acid biosynthesis to enhance defence against Phytophthora infestans in potato. Molecular Plant Pathology 21, 636–651. https://doi.org/10.1111/mpp.12919
10.1111/mpp.12919
CAS PubMed Web of Science® Google Scholar
Love, M.I., Huber, W., Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. https://doi.org/10.1186/s13059-014-0550-8
10.1186/s13059-014-0550-8
CAS PubMed Web of Science® Google Scholar
Maere, S., Heymans, K., Kuiper, M., 2005. BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics 21, 3448–3449. https://doi.org/10.1093/bioinformatics/bti551
10.1093/bioinformatics/bti551
CAS PubMed Web of Science® Google Scholar
Mao, P., Duan, M., Wei, C., Li, Y., 2007. WRKY62 Transcription Factor Acts Downstream of Cytosolic NPR1 and Negatively Regulates Jasmonate-Responsive Gene Expression. Plant and Cell Physiology 48, 833–842. https://doi.org/10.1093/pcp/pcm058
10.1093/pcp/pcm058
CAS PubMed Web of Science® Google Scholar
Marks, R.A., Smith, J.J., Cronk, Q., McLetchie, D.N., 2018. Variation in the bacteriome of the tropical liverwort, Marchantia inflexa, between the sexes and across habitats. Symbiosis 75, 93–101. https://doi.org/10.1007/s13199-017-0522-3
10.1007/s13199-017-0522-3
Web of Science® Google Scholar
Meldau, D.G., Meldau, S., Hoang, L.H., Underberg, S., Wunsche, H., Baldwin, I.T., 2013. Dimethyl Disulfide Produced by the Naturally Associated Bacterium Bacillus sp B55 Promotes Nicotiana attenuata Growth by Enhancing Sulfur Nutrition. The Plant Cell 25, 2731–2747. https://doi.org/10.1105/tpc.113.114744
10.1105/tpc.113.114744
CAS PubMed Web of Science® Google Scholar
Millet, Y.A., Danna, C.H., Clay, N.K., Songnuan, W., Simon, M.D., Werck-Reichhart, D., Ausubel, F.M., 2010. Innate Immune Responses Activated in Arabidopsis Roots by Microbe-Associated Molecular Patterns. The Plant Cell 22, 973–990. https://doi.org/10.1105/tpc.109.069658
10.1105/tpc.109.069658
CAS PubMed Web of Science® Google Scholar
Murillo-Williams, A., Munkvold, G.P., 2008. Systemic Infection by Fusarium verticillioides in Maize Plants Grown Under Three Temperature Regimes. Plant Disease 92, 1695–1700. https://doi.org/10.1094/PDIS-92-12-1695
10.1094/PDIS-92-12-1695
CAS PubMed Web of Science® Google Scholar
Mustroph, A., Zanetti, M.E., Jang, C.J.H., Holtan, H.E., Repetti, P.P., Galbraith, D.W., Girke, T., Bailey-Serres, J., 2009. Profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis. Proc. Natl. Acad. Sci. U.S.A. 106, 18843–18848. https://doi.org/10.1073/pnas.0906131106
10.1073/pnas.0906131106
CAS PubMed Google Scholar
Ngou, B.P.M., Wyler, M., Schmid, M.W., Kadota, Y., Shirasu, K., 2024. Evolutionary trajectory of pattern recognition receptors in plants. Nat Commun 15, 308. https://doi.org/10.1038/s41467-023-44408-3
10.1038/s41467-023-44408-3
CAS PubMed Google Scholar
Pescador, L., Fernandez, I., Pozo, M.J., Romero-Puertas, M.C., Pieterse, C.M.J., Martínez-Medina, A., 2022. Nitric oxide signalling in roots is required for MYB72-dependent systemic resistance induced by Trichoderma volatile compounds in Arabidopsis. Journal of Experimental Botany 73, 584–595. https://doi.org/10.1093/jxb/erab294
10.1093/jxb/erab294
CAS PubMed Web of Science® Google Scholar
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., Smyth, G.K., 2015. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47–e47. https://doi.org/10.1093/nar/gkv007
10.1093/nar/gkv007
CAS PubMed Web of Science® Google Scholar
Rudrappa, T., Biedrzycki, M.L., Kunjeti, S.G., Donofrio, N.M., Czymmek, K.J., Paul W., P., Bais, H.P., 2010. The rhizobacterial elicitor acetoin induces systemic resistance in Arabidopsis thaliana. Communicative & Integrative Biology 3, 130–138. https://doi.org/10.4161/cib.3.2.10584
10.4161/cib.3.2.10584
PubMed Google Scholar
Ryu, C.-M., Farag, M.A., Hu, C.-H., Reddy, M.S., Kloepper, J.W., Paré, P.W., 2004. Bacterial Volatiles Induce Systemic Resistance in Arabidopsis. Plant Physiology 134, 1017–1026. https://doi.org/10.1104/pp.103.026583
10.1104/pp.103.026583
CAS PubMed Web of Science® Google Scholar
Ryu, C.-M., Farag, M.A., Hu, C.-H., Reddy, M.S., Wei, H.-X., Paré, P.W., Kloepper, J.W., 2003. Bacterial volatiles promote growth in Arabidopsis. Proceedings of the National Academy of Sciences 100, 4927–4932.
10.1073/pnas.0730845100
CAS PubMed Web of Science® Google Scholar
Scharf, K.-D., Berberich, T., Ebersberger, I., Nover, L., 2012. The plant heat stress transcription factor (Hsf) family: Structure, function and evolution. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1819, 104–119. https://doi.org/10.1016/j.bbagrm.2011.10.002
10.1016/j.bbagrm.2011.10.002
CAS Web of Science® Google Scholar
Schmittgen, T.D., Livak, K.J., 2008. Analyzing real-time PCR data by the comparative CT method. Nat Protoc 3, 1101–1108. https://doi.org/10.1038/nprot.2008.73
10.1038/nprot.2008.73
CAS PubMed Web of Science® Google Scholar
Schulz-Bohm, K., Martín-Sánchez, L., Garbeva, P., 2017. Microbial Volatiles: Small Molecules with an Important Role in Intra- and Inter-Kingdom Interactions. Front. Microbiol. 8, 2484. https://doi.org/10.3389/fmicb.2017.02484
10.3389/fmicb.2017.02484
PubMed Google Scholar
Sharifi, R., Jeon, J.-S., Ryu, C.-M., 2022. Belowground plant–microbe communications via volatile compounds. Journal of Experimental Botany 73, 463–486. https://doi.org/10.1093/jxb/erab465
10.1093/jxb/erab465
CAS PubMed Web of Science® Google Scholar
Shuai, B., Reynaga-Peña, C.G., Springer, P.S., 2002. The Lateral Organ Boundaries Gene Defines a Novel, Plant-Specific Gene Family. Plant Physiology 129, 747–761. https://doi.org/10.1104/pp.010926
10.1104/pp.010926
CAS PubMed Web of Science® Google Scholar
Sun, T., Zhang, Yaxi, Li, Y., Zhang, Q., Ding, Y., Zhang, Yuelin, 2015. ChIP-seq reveals broad roles of SARD1 and CBP60g in regulating plant immunity. Nat Commun 6, 10159. https://doi.org/10.1038/ncomms10159
10.1038/ncomms10159
Google Scholar
Tena, G., 2020. From algae to land plants. Nat. Plants 6, 594–594. https://doi.org/10.1038/s41477-020-0712-5
10.1038/s41477-020-0712-5
PubMed Google Scholar
Trivedi, P., Leach, J.E., Tringe, S.G., Sa, T., Singh, B.K., 2020. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol 18, 607–621. https://doi.org/10.1038/s41579-020-0412-1
10.1038/s41579-020-0412-1
CAS PubMed Web of Science® Google Scholar
Vlot, A.C., Sales, J.H., Lenk, M., Bauer, K., Brambilla, A., Sommer, A., Chen, Y., Wenig, M., Nayem, S., 2021. Systemic propagation of immunity in plants. New Phytologist 229, 1234–1250. https://doi.org/10.1111/nph.16953
10.1111/nph.16953
CAS PubMed Web of Science® Google Scholar
Wang, D., Amornsiripanitch, N., Dong, X., 2006. A Genomic Approach to Identify Regulatory Nodes in the Transcriptional Network of Systemic Acquired Resistance in Plants. PLoS Pathog 2, e123. https://doi.org/10.1371/journal.ppat.0020123
10.1371/journal.ppat.0020123
CAS PubMed Web of Science® Google Scholar
Wani, S.H., Anand, S., Singh, B., Bohra, A., Joshi, R., 2021. WRKY transcription factors and plant defense responses: latest discoveries and future prospects. Plant Cell Rep 40, 1071–1085. https://doi.org/10.1007/s00299-021-02691-8
10.1007/s00299-021-02691-8
CAS PubMed Web of Science® Google Scholar
Wenke, K., Wanke, D., Kilian, J., Berendzen, K., Harter, K., Piechulla, B., 2012. Volatiles of two growth-inhibiting rhizobacteria commonly engage AtWRKY18 function. The Plant Journal 70, 445–459. https://doi.org/10.1111/j.1365-313X.2011.04891.x
10.1111/j.1365-313X.2011.04891.x
CAS PubMed Web of Science® Google Scholar
Wicaksono, W.A., Semler, B., Pöltl, M., Berg, C., Berg, G., Cernava, T., 2023. The microbiome of Riccia liverworts is an important reservoir for microbial diversity in temporary agricultural crusts. Environmental Microbiome 18, 46. https://doi.org/10.1186/s40793-023-00501-0
10.1186/s40793-023-00501-0
CAS PubMed Google Scholar
Xie, C., Zhou, X., Deng, X., Guo, Y., 2010. PKS5, a SNF1-related kinase, interacts with and phosphorylates NPR1, and modulates expression of WRKY38 and WRKY62. Journal of Genetics and Genomics 37, 359–369. https://doi.org/10.1016/S1673-8527(09)60054-0
10.1016/S1673-8527(09)60054-0
CAS PubMed Web of Science® Google Scholar
Yasmin, H., Rashid, U., Hassan, M.N., Nosheen, A., Naz, R., Ilyas, N., Sajjad, M., Azmat, A., Alyemeni, M.N., 2021. Volatile organic compounds produced by Pseudomonas pseudoalcaligenes alleviated drought stress by modulating defense system in maize ( Zea mays L.). Physiologia Plantarum 172, 896–911. https://doi.org/10.1111/ppl.13304
10.1111/ppl.13304
CAS PubMed Web of Science® Google Scholar
Yuan, X., Wang, H., Cai, J., Li, D., Song, F., 2019. NAC transcription factors in plant immunity. Phytopathol Res 1, 3. https://doi.org/10.1186/s42483-018-0008-0
10.1186/s42483-018-0008-0
Web of Science® Google Scholar
Zamioudis, C., Korteland, J., Van Pelt, J.A., Van Hamersveld, M., Dombrowski, N., Bai, Y., Hanson, J., Van Verk, M.C., Ling, H., Schulze-Lefert, P., Pieterse, C.M.J., 2015. Rhizobacterial volatiles and photosynthesis-related signals coordinate MYB 72 expression in Arabidopsis roots during onset of induced systemic resistance and iron-deficiency responses. The Plant Journal 84, 309–322. https://doi.org/10.1111/tpj.12995
10.1111/tpj.12995
CAS PubMed Web of Science® Google Scholar
Zamioudis, C., Mastranesti, P., Dhonukshe, P., Blilou, I., Pieterse, C.M.J., 2013. Unraveling Root Developmental Programs Initiated by Beneficial Pseudomonas spp. Bacteria. Plant Physiology 162, 304–318. https://doi.org/10.1104/pp.112.212597
10.1104/pp.112.212597
CAS PubMed Web of Science® Google Scholar
Zhang, H., Sun, Y., Xie, X., Kim, M., Dowd, S.E., Paré, P.W., 2009. A soil bacterium regulates plant acquisition of iron via deficiency-inducible mechanisms. The Plant Journal 58, 568–577. https://doi.org/10.1111/j.1365-313X.2009.03803.x
10.1111/j.1365-313X.2009.03803.x
CAS PubMed Web of Science® Google Scholar

Volume176, Issue6

November/December 2024

e70002

Filename	Description
ppl70002-sup-0001-Figures.docxWord 2007 document , 1.4 MB	Data S1
ppl70002-sup-0002-Tables.xlsxExcel 2007 spreadsheet , 352 KB	Data S2

Comparison of early transcriptomic changes to diverse microbial volatiles in Arabidopsis thaliana

Abstract

1 INTRODUCTION

2 MATERIALS AND METHODS

2.1 Plant growth condition and mVOCs exposure experiment

2.2 Microbial Identification and Cultural Condition

2.3 Volatile compound analysis of PGIVs and PGPVs