• effector;
  • plant defence;
  • secondary metabolism;
  • serotonin;
  • SnToxA;
  • Stagonospora nodorum ;
  • wheat (Triticum aestivum)


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Stagonospora nodorum and Pyrenophora tritici-repentis produce the effector ToxA that interacts with the dominant susceptibility gene in wheat, Tsn1. However, the way in which ToxA induces cell death and causes disease is unclear.
  • Here, we performed comprehensive metabolite profiling of ToxA-infiltrated wheat (Triticum aestivum) to observe the secondary metabolite response to this effector.
  • A strong induction of secondary metabolism subsequent to SnToxA infiltration was observed, including the monoamine serotonin. We established a novel role for serotonin as a phytoalexin in wheat and demonstrated that serotonin strongly inhibited sporulation of S. nodorum. Microscopy revealed that serotonin interferes with spore formation and maturation within pycnidial structures of the fungus. Subsequent analysis of S. nodorum exposed to serotonin revealed metabolites changes previously associated with sporulation, including trehalose and alternariol. Furthermore, we identified significantly lower concentrations of serotonin during infection compared with infiltration with ToxA, providing evidence that S. nodorum may suppress plant defence.
  • This is the first study demonstrating induction of plant secondary metabolites in response to a necrotrophic effector that have significant antifungal potential against the pathogen. While it is generally accepted that necrotrophs exploit host cell responses, the current research strengthens the notion that necrotrophs require mechanisms to overcome plant defence to survive initial stages of infection.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Owing to the immobile nature of plants and their inability to escape pathogens, plants have evolved complex defence mechanisms to protect themselves against threats. Plant defence is often separated into two distinct types. The first recognizes pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors and this activates PAMP-triggered immunity (PTI) and basal defence responses (Jones & Dangl, 2006). Basal defence is a broad range of nonspecific defence responses, which in the majority of cases is effective in preventing further colonization by a wide range of pathogens (Dangl & Jones, 2001). However, certain pathogens have evolved the ability to produce effectors, which overcome PTI, leading to effector-triggered susceptibility (ETS). Here, the second type of plant defence, called effector-triggered immunity (ETI), can be activated. This is initiated inside the cell by the recognition of pathogen effectors by plant resistance-gene products, often nucleotide binding site (NBS)-leucine rich repeat (LRR) proteins (Jones & Dangl, 2006). This defence mechanism involves a hypersensitive response and localized host cell death and is therefore successful against biotrophic pathogens that require living host tissue to survive.

The host mechanisms, though governing the resistance and susceptibility to necrotrophic pathogens, are less well understood. Recently, however, several necrotrophs have been shown to secrete host-specific toxins (HSTs) or effector proteins that play a crucial role in the outcome of disease (Wolpert et al., 2002; Oliver & Solomon, 2010). A single dominant gene often confers host susceptibility to these effectors and the presence of both the susceptibility gene and effector results in ETS and corresponding host cell death, therefore causing disease. Thus it would appear that rather than avoid recognition, necrotrophs willingly exploit host cell death mechanism(s) for their own nutritional gain and subsequent disease success. Hence, interactions between necrotrophic effectors and plant susceptibility genes are termed inverse gene-for-gene interactions (Oliver & Solomon, 2010) and the discovery of ETS in necrotrophic interactions has revealed that these pathogens have evolved more sophisticated mechanisms of causing cell death in their host than originally thought.

The best characterized of these necrotrophic host-specific effector proteins is PtrToxA, originally discovered in Pyrenophora tritici-repentis, the causal agent of tan spot of wheat (Ballance et al., 1989; Tomas et al., 1990; Tuori et al., 1995; Zhang et al., 1997). PtrToxA is a 13.2 kDa protein (Tuori et al., 1995) and has a unique protein sequence harbouring no known functional protein motif (Manning & Ciuffetti, 2005). PtrToxA is internalized in a light-dependent manner into sensitive host mesophyll cells containing Tsn1 (Manning & Ciuffetti, 2005) via an unknown mechanism (Manning et al., 2008). Yeast two-hybrid experiments indicate that the Tsn1 protein does not directly interact with PtrToxA and a receptor has not been identified (Faris et al., 2010). Once internalized, PtrToxA is localized to the chloroplast where it interacts with a chloroplast membrane-associated protein designated ToxA binding protein-1 (ToxABP-1; Manning et al., 2007). Recent microarray studies have established major transcriptional reprogramming in wheat following PtrToxA infiltration (Adhikari et al., 2009; Pandelova et al., 2009). These changes include the induction of plant defence responses, impairment of photosynthetic machinery and increased responses to oxidative stress. However, the role of PtrToxA in inducing these responses is unknown.

Recently, genome sequence analysis of the related wheat pathogen Stagonospora nodorum (Hane et al., 2007) identified a PtrToxA homologue (SnToxA) sharing 99.7% sequence identity, and evidence exists for the lateral transfer of this gene from S. nodorum to P. tritici-repentis some time before 1941 (Friesen et al., 2006). Preliminary studies of SnToxA have indicated that SnToxA and PtrToxA have comparable modes of action and the same gene confers susceptibility (Friesen et al., 2006; Vincent et al., 2011). Although a susceptibility gene, Tsn1 contains features typically associated with classical resistance (R) genes, including NBS and LRR domains (Faris et al., 2010). Two additional dominant susceptibility genes in other species have also been identified to contain these R gene-associated domains; LOV1 in Arabidopsis confers susceptibility to the victorin effector produced by Cochliobolus victoriae (Lorang et al., 2007) and Pc in sorghum confers susceptibility to the PC toxin produced by Periconia circinata (Nagy & Bennetzen, 2008). The cloning and identification of these genes have outlined the similarities between host responses that lead to resistance against biotrophs and those that result in susceptibility to necrotrophs (Lorang et al., 2007; Hammond-Kosack & Rudd, 2008).

A major component of successful plant defence mechanisms is the production of antimicrobial secondary metabolites which are either preformed (phytoanticipans) or pathogen/stress-induced (phytoalexins; Du Fall & Solomon, 2012). It is yet to be established whether these metabolites play a role in SnToxA-induced cell death in wheat. In this study, we undertook comprehensive metabolite profiling of wheat extracts to determine the effect of SnToxA on the secondary metabolism of wheat. LC-MS has been used successfully over recent years to measure hundreds of plant secondary metabolites unable to be detected using more traditional GC-MS methods (von Roepenack-Lahaye et al., 2004; Kanno et al., 2010; Spagou et al., 2010). This technology has enabled a powerful approach to investigate the changes in plant secondary metabolism induced by SnToxA in order to understand the mechanisms behind SnToxA-induced cell death. Plant metabolites altered significantly by SnToxA were functionally characterized in terms of their activity against S. nodorum. This led to the discovery of a number of pathways induced by SnToxA that produce metabolites with novel antifungal activity. These results hold substantial potential for control of S. nodorum by the agricultural community in addition to contributing to our understanding of necrotrophic effectors and ETS.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Plant growth and infiltration

SnToxA-sensitive wheat cultivars (Triticum aestivum L. genotype BG261 and Grandin) and the SnTox3-sensitive cultivar (BG220) were grown from seed as described previously (Solomon et al., 2006a). Purified SnToxA protein was kindly provided by Richard Oliver's laboratory (Curtin University, Australia). The second leaf of 10-d-old plants was infiltrated with distilled water or purified SnToxA (1.22 μg ml−1) at the beginning of the photoperiod (Liu et al., 2004). For SnTox3-infiltrated BG220, the histidine-tagged SnTox3 protein was purified using a Pichia pastoris expression system. The SnTox3 and empty vector control samples were purified by Fast protein liquid chromatography (FPLC) using a histidine-tag affinity column (GE Healthcare, unpublished). The concentration of SnTox3 was adjusted so that infiltration of the protein caused comparable amounts of chlorosis and necrosis over the same time course as SnToxA. Plants were harvested at 4, 12, 24, 48 and 72 h postinfiltration (hpi). Six biological replicates, each consisting of eight infiltrated sections, were harvested and metabolism immediately quenched by snap-freezing in liquid nitrogen. Tissue was stored at −80°C until extraction. For LC-MS analysis of metabolite concentrations in infected tissue, plants were infected with a 5 μl spore suspension (1 × 106 spores ml−1), left in a dark humid environment for 2 d before returning to regular glasshouse conditions. Infected tissue was harvested at 7 d postinfiltration (dpi).

Extraction of semipolar wheat metabolites

Six replicate samples were kept frozen and homogenized (2 min, 20 Hz) in a TissueLyser (Qiagen). Frozen tissue powder (c. 60 mg) was extracted with 1 ml 80% methanol at 4°C with shaking (45 min, 900 rpm). Samples were centrifuged to pellet cell debris (20 000 g, 10 min), the supernatant was transferred to a fresh tube and dried under vacuum (Labconco centrivap, Kansas City, MO, USA). Immediately becore LC-MS analysis, metabolite samples were resuspended in 80% methanol, centrifuged (20 000 g, 10 min) and 200 μl was transferred to an autosampler vial.

LC-MS analysis

Liquid chromatography-mass spectrometry analysis was undertaken on an Agilent 1200 series high-performance liquid chromatograph (Agilent, Santa Clara, CA, USA) equipped with an autosampler, degasser and binary pump. Chromatographic separation was performed at room temperature using a ZORBAX Eclipse RRHD C18 column (1.8 μm particle size, 2.1 mm i.d. × 150 mm; Agilent) and an in-line filter. The mobile phase consisted of a linear gradient of 95% eluent A (0.1% (v/v) formic acid in deionized water) to 100% eluent B (0.1% (v/v) formic acid in 90% acetonitrile) over 50 min at a flow rate of 200 μl min−1. The gradient was followed by a 5 min hold at 100% eluent B, back to 95% eluent A over 5 min and a 10 min re-equilibration. An Agilent 6520 quadrupole time-of-flight (QToF) system with a Jetstream electrospray ionization (ESI) source was coupled to the LC for accurate mass detection. ESI conditions were as follows: nebulizer pressure, 35 psi; gas flow rate, 10 l min−1; gas temperature, 350°C; capillary voltage, 3500 V; fragmentor, 175 V; and skimmer, 65 V. The instrument was operated in the 2 GHz extended dynamic range mode and data were collected in the m/z range 70–1000 amu. For comprehensive analysis, positive and negative ion modes were employed. Data processing was performed with Mass Profiler Professional software (Agilent).

LC-MS data analysis

Liquid chromatography-mass spectrometry data deconvolution and peak finding were performed using the MassHunter Qualitative software (Agilent). The molecular feature algorithm extracted peaks over a threshold of 1000 counts. Peak alignment, normalization to total area and statistical analysis were performed using MassHunter Mass Profiler Professional software (Agilent). Putative metabolite identifications were assigned using the publicly available MassBank (Japan), ReSpect (RIKEN Plant Science Center) and METLIN (Scripps Centre for Metabolomics and Mass Spectrometry) databases.

Fungal growth

Stagonospora nodorum wild-type strain SN15 was provided by the Department of Agriculture and Food, Government of Western Australia, South Perth, WA, Australia. The fungus was routinely grown on V8 PDA plates as previously described (Solomon et al., 2004). For mycelial growth assays requiring controlled growth conditions, S. nodorum SN15 was used to inoculate minimal medium (MM) as previously described (Solomon et al., 2004). Radial growth was measured every other day for 2 wk. For sporulation assays, spores were isolated and counted 14 dpi.

Detached leaf assays

The ability of S. nodorum SN15 to grow and sporulate in vivo was assayed on detached leaves from 2-wk-old wheat seedlings, using a method modified from that described by Benedikz et al. (1981). Briefly, the distal ends (2 cm) of the detached wheat leaves were removed and the next 4 cm portions were embedded into agar (15 g l−1) adaxial side up. Concentrations of serotonin and 6-methoxy-2-benzoxazolinone (MBOA) ranging from 0.1 to 10 mM were dissolved in water and ethanol (0.5% final concentration), respectively, and added to the agar after autoclaving. Detached leaves were inoculated with S. nodorum SN15 spores (10 μl, 1 × 106 spores ml−1) containing 0.02% Tween20 and the leaves were incubated under 12 : 12 h light : dark conditions at 25°C. Spores were isolated and counted at 14 dpi by emerging leaves in water for 10 min before straining suspension through glass wool to remove mycelia. For LC-MS analysis of detached leaves, 2-wk-old leaves were placed in agar with and without 1 mM serotonin. The control plate was inoculated with S. nodorum spores (5 μl, 1 × 106 ml−1). Three leaves were collected per replicate at 72 h, placed in 2 ml tubes and immediately frozen in liquid nitrogen.

Extraction of fungal metabolites for GC-MS analysis

Fungal mycelia were scraped from plates at 13 d after inoculation, frozen in liquid nitrogen to quench metabolism and lyophilized. Weights were recorded and mycelia transferred to a 2 ml safelock tube with a 3 mm ball bearing. Methanol (685 μl, −40°C) containing ribitol (75 μl, 50 μM) was added and mycelia disrupted using a tissue lyser (25 Hz, 2 min). Tubes were frozen in liquid nitrogen and thawed on ice to further disrupt cells. After vortexing briefly, cell debris was collected by centrifugation (20 000 g, 2 min). Supernatant was transferred to a fresh tube, vortexed and centrifuged again. A 2 mg of equivalent of fungal tissue was transferred to a glass vial and dried using a centrivap (Labconco).

Derivatization was performed online with the Gerstel MultiPurpose Sampler (Linthicum, MD, USA). Methoximation of carbonyl groups was achieved with incubation at 37°C and shaking (90 min, 1200 rpm) with methylamine-HCl (20 μl, 20 mg ml−1 in pyridine). Trimethylsilyation of polar groups was completed by the addition of 30 μl of N-trimethylsilyl-N-methyl trifluoroacetamide (MSTFA), followed by incubation at 37°C and shaking (30 min, 1200 rpm). GC-MS analysis was undertaken as described previously (Lowe et al., 2009). Samples were analysed in 20 : 1 split mode. Metabolite abundances were normalized to the ribitol internal standard and sample weights using AnalyzerPro (SpectralWorks Ltd, Runcorn, Cheshire, UK). Peak identification was performed using the publicly available Golm metabolome database (Max-Planck-Institute for Plant Physiology; Schauer et al., 2005) and the commercial National Institute of Standards and Technology 08 (NIST; mass spectral library and confirmed using databases generated in-house. Multivariate data analyses were carried out using The Unscrambler (version 10.0, CAMO ASA, Oslo, Norway) and the JMP 8.0.1 statistical package (SAS Institute Inc., Cary, NC, USA). Metabolites deemed to be significant (> 0.05), as determined by ANOVA for treatment and treatment × time, were combined. A false discovery rate (FDR) method was applied (Benjamini & Hochberg, 1995).


Fungal culture morphology was observed using 5 mm blocks of MM plates fixed in paraformalehyde as described previously (Škalamera & Hardham, 2006). The blocks were placed in liquid Tissue-Tek (Miles Inc., Elkhart, IN, USA) inside a plastic mould (Cryomold; Miles Inc.) and plunged into liquid nitrogen. The frozen block was stored at −20°C for 24 h before sectioning. Sectioning was performed on a Reichert cryotome (2800 Frigocut E, Depew, NY, USA) set to cut 14 μm sections. The sections were transferred to a gelatin-coated glass slide and left for 10 min before staining. Tissue-Tek was removed by dipping the slides briefly into water and air-drying. Staining was carried out with toluidine blue in benzoate buffer (pH 4.4) for 2–3 min followed by thorough rinsing with water. Sections were viewed using an Axioplan universal microscope (Zeiss).


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

LC-MS analysis of semipolar metabolites in SnToxA wheat

Data of four biological replicates were acquired in positive and negative ESI modes on the LC-QToF for water-infiltrated and SnToxA-infiltrated wheat at five time points, thereby generating two comprehensive datasets. For simplicity, these positive and negative mode data will be referred to as datasets 1 and 2, respectively. A representative chromatogram is shown in Supporting Information, Fig. S1. Using the software and algorithms outlined in the 'Materials and Methods' section, 131 and 159 mass features were identified in datasets 1 and 2, respectively, that were present in at least three of the four biological replicates and had a minimum twofold change.

Principal component analysis (PCA) was used to discover trends within the dataset and determine data quality. Nearly all of the biological replicates clustered together, verifying the experimental design and robustness of the method. The PCA plots illustrate that the largest variations accounted for by principal component 1 (PC1) are 28.41 and 25.68% for datasets 1 and 2, respectively (Fig. 1a,b). In both cases, the variation in PC1 can be explained by the difference between water- and SnToxA-infiltrated wheat at 48 and 72 hpi. PC2 accounted for 16.75 and 15.77% for datasets 1 and 2, respectively, and can be partially explained by the time points at which samples were collected. The 20 mass features with the highest contribution to PC1 for datasets 1 and 2 are shown in Fig. 1(c) and (d), respectively. In dataset 1, two of the 10 mass features with negative PC1 values (corresponding to a higher abundance in SnToxA 48 and 72 h wheat extracts) belong to the putatively identified 2-O-β-d-glucopyranosyloxy-4,7-dimethoxy-(2H)-1,4-benzoxazin-3(4H)-one (HMDBOA-glc). Four of the 10 features with positive PC1 values (corresponding to lower abundance in SnToxA 48 and 72 h wheat extracts) belong to the putatively identified 2-O-β-d-glucopyranosyloxy-4-hydroxy-7-(2H)-methoxy-1,4-benzoxazin-3(4H)-one (DIMBOA-glc; Fig. 1c). The remaining mass features in this list were unidentified. The 20 metabolites with the highest contribution to the variation between controls and SnToxA-infiltrated wheat in dataset 2 were unable to be assigned putative identifications (Fig. 1d).


Figure 1. Principal component analysis (PCA) of LC-MS metabolite profiling data of SnToxA-infiltrated wheat (Triticum aestivum). PCA of LC-MS data collected in positive (a) and negative mode (b) electrospray ionization and the corresponding factor loadings illustrating the 20 metabolites with the highest contribution to the variation in principal component 1 (PC1) in each dataset (c and d, respectively). DIMBOA-glc, 2-O-β-d-glucopyranosyloxy-4-hydroxy-7-(2H)-methoxy-1,4-benzoxazin-3(4H)-one; MBOA, 6-methoxy-2-benzoxazolinone.

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In order to statistically validate the abundance changes identified in the PCA, univariate and multivariate statistical analyses were performed on the datasets; a representative table is presented in Table S1. Statistical analysis identified 88 and 111 mass features to be significantly altered in SnToxA-infiltrated wheat relative to the control in datasets 1 and 2, respectively (< 0.05). The MassHunter formula generator algorithm and accurate mass information were used to generate empirical formulas for a subset of the peaks of interest using the abundances of carbon isotopes present in the molecular ion. Putative metabolite identifications were established by searching these empirical formulas against the publicly available MassBank, ReSpect and METLIN online metabolite databases.

Two compounds of interest identified were the monoamine serotonin (5-hydroxytryptamine) and the benzoxazinoid MBOA, which were both significantly more abundant (= 4.5 × 10−11 and 1.9 × 10−4, respectively) in SnToxA-infiltrated wheat at 48 and 72 hpi (Fig. 2a,b). Pure standards were used to confirm the putative identifications using tandem mass spectrometry to obtain MS/MS fragmentation spectra of the molecular ions (Fig. 2c–f). Serotonin and MBOA are both synthesized via the tryptophan pathway (Fig. 3a) and a number of the intermediates and derivatives of these metabolites were also significantly altered in abundance in response to SnToxA. The serotonin precursors tryptophan and tryptamine were significantly more abundant in SnToxA-infiltrated wheat in addition to the two derivatives, feruloyltryptamine and feruloylserotonin (Fig. 3b). Similarly, the two MBOA precursors, DIMBOA-glc and HMDBOA-glc, were also altered in abundance following SnToxA infiltration. The former decreased in abundance and the latter increased, indicating that reserves of DIMBOA-glc are being degraded and converted into the active aglycone (Fig. 3c). No other significant changes in other indole compounds were observed.


Figure 2. Identification of significant increases in abundance of serotonin and 6-methoxy-2-benzoxazolinone (MBOA) in SnToxA-infiltrated wheat (Triticum aestivum). (a, b) Extracted ion chromatogram (EIC) of m/z 177.1021 showing significant increase of putative serotonin (a) and m/z 166.0498 showing significant increase of putative MBOA (b) in four replicates of wheat infiltrated with SnToxA (red) or water (blue) (< 0.0001). The insets show the mass spectra of these features. (c–f) Putative identifications were confirmed by obtaining MS/MS fragmentation spectra of putative serotonin (c) and MBOA (d) identical to the purchased standards (e and f, respectively).

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Figure 3. SnToxA infiltration leads to the induction of the tryptophan pathway in wheat (Triticum aestivum). (a) A simplified pathway showing the synthesis of 6-methoxy-2-benzoxazolinone (MBOA) and serotonin via the tryptophan metabolic pathway in plants. (b) The abundance of serotonin and derivatives increase in SnToxA-infiltrated wheat at 48 and 72 h. The abundance of these metabolites in water-infiltrated controls was below the limits of detection of the data-mining algorithms used. (c) 2-O-β-d-Glucopyranosyloxy-4-hydroxy-7-(2H)-methoxy-1,4-benzoxazin-3(4H)-one (DIMBOA-glc) decreases significantly (*, < 0.05; **, < 0.01) in SnToxA-infiltrated wheat at 48 and 72 h while 2-O-β-d-glucopyranosyloxy-4,7-dimethoxy-(2H)-1,4-benzoxazin-3(4H)-one (HMDBOA) and MBOA increased significantly. Error bars represent the SEM metabolite abundance at each time point (= 3).

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Serotonin inhibits sporulation of S. nodorum in vitro, while MBOA significantly reduces fungal growth and inhibits spore germination

Given the increasing abundances of serotonin and MBOA, we questioned whether these compounds would affect fungal growth. To do this, MM plates containing various concentrations of serotonin and MBOA were inoculated with S. nodorum (Fig. 4a and b respectively). Measurements of mycelial growth taken over a 2 wk period indicated that serotonin had no effect on fungal vegetative growth at 0.1, 1 or 3 mM at 2 wk (Fig. 4c). In the presence of 10 mM serotonin, radial growth progressed at a slower rate and by 2 wk had almost ceased completely. Subsequent spore counts at 2 wk postinoculation demonstrated that serotonin significantly inhibited in vitro sporulation (< 0.05) at 0.1, 1 and 3 mM, with up to 50-fold fewer spores than controls (Fig. 4e). At 10 mM serotonin, the spore concentration was too low to count.


Figure 4. Growth and sporulation assays of Stagonospora nodorum with exogenous serotonin and 6-methoxy-2-benzoxazolinone (MBOA) in vitro. (a, b) Photographs illustrating mycelial growth of S. nodorum grown for 2 wk on minimal media (MM) containing a range of concentrations of serotonin (a) or MBOA (b). (c, d) Radial growth measurements of S. nodorum grown on various concentrations of serotonin (c) or MBOA (d). Error bars represent the SEM at each time point (= 3). (e) Spore counts of S. nodorum isolated from serotonin plates at 2 wk postinoculation showing significant inhibition of sporulation from 0.1 mM (< 0.001). Error bars represent the SEM at each concentration (= 3). Letters A–C represent significant differences.

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The addition of MBOA to MM plates correlated with a significantly reduced growth of S. nodorum (Fig. 4b). Radial measurements of fungal growth found a significant (< 0.001) reduction at 0.3 and 1 mM at 14–20 dpi; however, there was no significant inhibition at 0.1 mM MBOA (Fig. 4d). Furthermore, at 3 mM MBOA, spore germination was completely inhibited. No sporulation was observed in the presence or absence of any of the tested concentrations of MBOA or the control by 20 d; this was a result of the 1.6% final concentration of ethanol present in the MM required to dissolve the compound. Such concentrations of ethanol have previously been observed in our laboratory to impair sporulation.

The concentrations of serotonin demonstrated to inhibit sporulation of S. nodorum in vitro are comparable to those present in infected tissue

The abundance of serotonin significantly increased in SnToxA-infiltrated wheat and this compound was subsequently found to strongly inhibit sporulation of S. nodorum when added to the growth media. Before examining the activity of serotonin in planta, we determined whether the plant produces serotonin during an infection and how this compares to the amounts that accumulate if we apply exogenous serotonin to detached leaves. Detached leaf assays (DLAs) and LC-MS were utilized to measure the relative concentrations of serotonin in leaves at 72 hpi. The results confirmed that serotonin does in fact accumulate during an infection and were comparable to the amount accumulated in leaves at 72 h after exogenous application of 1 mM serotonin (Fig. S2).

Serotonin inhibits sporulation of S. nodorum in planta

Detached leaf assays (DLAs) were used to assess the effect of serotonin on the pathogenicity of S. nodorum. Detached leaves were placed in agar containing 0.1, 0.3, 1 and 3 mM serotonin and the leaves were inoculated with a S. nodorum spore suspension. Two weeks after inoculation, there was no discernible difference in lesion development in the presence or absence of serotonin (Fig. 5a–f). The infected leaves were then used to determine the effect of serotonin on sporulation in vivo. When observed under a dissecting microscope, the numbers of pycnidia on control leaves and leaves in the presence of serotonin were similar (Fig. 5g–k). On control leaves and at 0.1 and 0.3 mM serotonin, the cirrhus containing pink pycnidospores can clearly be seen emerging from the pycnidia on the surface of the leaf (Fig. 5g–i). However, at 1 and 3 mM serotonin pycnidia seem to remain beneath the leaf surface and all cirrhus appears clear in colour (Fig. 5j–k). Subsequent counting of the pycnidiospores revealed that serotonin significantly impaired the ability of S. nodorum to produce pycnidiospores from 0.3 mM (< 0.005; Fig. 5l). A 12-fold inhibition of sporulation was observed at 1 and 3 mM serotonin, confirming that exogenous serotonin also impairs sporulation in planta. A comparable analysis with MBOA revealed that it had no effect on the asexual development of S. nodorum in planta (Fig. S3).


Figure 5. Effect of serotonin on sporulation of Stagonospora nodorum in vitro. (a–f) Detached leaf assays (DLAs) showing S. nodorum-inoculated leaf sections placed in agar containing illustrated concentrations of serotonin. (g–k) Microscopy (×10) showing phenotypic differences in pycnidial structures and exuding pink cirrhus containing pycnidiospores at various concentrations of serotonin. (l) Spore counts of S. nodorum isolated from DLAs 2 wk postinoculation show significant inhibition of sporulation from 0.3 mM (< 0.005). Error bars represent the SEM at each concentration (= 3). P, pycnidium; C, cirrhus. Letters A–C represent significant differences.

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Serotonin impairs the maturation of pycnidia

Exposure of S. nodorum to serotonin revealed that whilst the numbers of pycnidia were unaffected, there were significantly fewer pycnidiospores produced. To better understand this phenotype, we performed cryosectioning and microscopy of the pycnidial structures formed on MM plates. Pycnidia of S. nodorum that formed on control plates appeared normal in size and structure and contained mature pycnidiospores (Fig. 6a). By contrast, pycnidia from plates containing 0.3 or 1 mM serotonin were strikingly different, with a number of pycnidia containing no visible mature spores. The pycnidial wall and subparietal layer were evident; however, the pycnidial cavity was devoid of both spores and conidiogenesis cells, suggesting that serotonin specifically blocks the maturation of the pycnidia at a distinct stage subsequent to the formation of the pycnidial wall. It is significant to note that many of the pycnidia examined microscopically demonstrated this phenotype in the presence of serotonin (Fig. 6b,c).


Figure 6. Microscopy reveals that serotonin disrupts spore formation within the pycnidial structures. (a–c) Light microscopy of agar cross-sections illustrating pycnidia of Stagonospora nodorum grown on minimal media (a) and media supplemented with 0.3 mM (b) and 1 mM serotonin (c). W, pycnidial wall; C, cirrhus; S, spore; SL, subparietal layer.

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Metabolites previously associated with sporulation are affected by serotonin exposure

Gas chromatography-mass spectrometry metabolite profiling has previously been utilized to investigate sporulation mutants of S. nodorum and has established the essential role of specific primary metabolic pathways in asexual sporulation (Solomon et al., 2006c; Lowe et al., 2009). Consequently, we performed untargeted metabolite profiling to determine whether changes in primary metabolism were contributing to the inability of S. nodorum to differentiate in the presence of serotonin. GC-MS was used to analyse polar metabolites extracted from wild-type S. nodorum grown for 2 wk on MM containing 0.3 or 1 mM serotonin, concentrations previously found to significantly inhibit sporulation in vitro and in vivo.

Principal component analysis was used to discover trends within the dataset and provide a means to observe the quality of data obtained from biological replicates (Fig. 7a). The replicates of the control (group 1) and the replicates of the pathogen with serotonin (group 2) were clearly separated by PC1, which explained 68% of the variance. PC2 accounted for 12% of the variance between samples and can be explained by natural biological variation between the replicates. Factor loadings of PC1 revealed trehalose to be the principal metabolite responsible for the negative variation in PC1, corresponding to a decrease of this metabolite in S. nodorum grown in the presence of serotonin (Fig. 7b). The largest positive factor loadings for PC1 were fructose, pyroglutamate and alternariol. These metabolite changes are clearly demonstrated by the raw chromatograms (Fig. 7c). Multivariate statistical analysis revealed the abundance of 56 of the 187 metabolites detected to be significantly different (< 0.05) in serotonin samples relative to the control (Table 1). Of these, 32 were identified with the NIST08 library or the Golm Metabolome Database and confirmed with in-house databases. Two-thirds of the 56 significant metabolites were more abundant in fungus grown in the presence of serotonin. It was significant to note that mannitol concentrations, a metabolite crucial for S. nodorum sporulation, were unaffected.

Table 1. List of fungal metabolites with significant abundance changes in response to serotonin
RT_RI_BP_Metabolite IDControl ± SE0.3 mM serotonin ± SE1 mM serotonin ± SE
  1. Bold values represent a significant difference from the controls (< 0.05). Metabolites are annotated as retention time_retention index_base peak_metabolite ID.

10.18_1092_174_Unknown22.31 ± 4.1512.01 ± 3.2014.39 ± 0.98
10.2_1093_L-Alanine19.84 ± 1.6520.82 ± 8.9026.46 ± 3.34
10.23_1094_174_Unknown17.95 ± 6.1710.82 ± 4.780.00 ± 0.00
11.26_1128_130_Unknown23.38 ± 7.4918.20 ± 7.820.00 ± 0.00
13.68_1211_144_L-Valine2.42 ± 1.0012.63 ± 1.799.25 ± 1.47
14.89_1260_132_L-Serine2.90 ± 0.736.72 ± 0.504.04 ± 1.11
15.05_1266_174_Unknown8.67 ± 1.1528.35 ± 2.3924.41 ± 5.42
15.35_1278_147_Glycerol2.86 ± 0.8327.29 ± 7.996.61 ± 2.24
15.86_1299_117_L-Threonine3.38 ± 0.299.05 ± 0.765.23 ± 1.10
16.08_1308_174_Glycine0.00 ± 0.008.67 ± 1.006.90 ± 2.04
16.44_1323_75_Succinate4.78 ± 2.0114.44 ± 2.7512.85 ± 2.26
18.15_1392_218_L-Threonine3.11 ± 0.8911.14 ± 1.656.90 ± 1.31
19.1_1431_160_Unknown9.08 ± 0.746.04 ± 0.502.98 ± 1.07
20.04_1469_281_Unknown6.16 ± 1.710.00 ± 0.005.22 ± 2.50
20.85_1502_191_Unknown16.93 ± 2.9912.87 ± 0.928.41 ± 1.03
20.93_1507_217_Erythritol4.70 ± 0.829.55 ± 1.537.08 ± 1.21
21.24_1524_156_Pyroglutamic acid40.06 ± 2.27117.68 ± 9.52108.07 ± 22.49
21.39_1532_174_4-Aminobutyric acid0.00 ± 0.004.00 ± 0.711.89 ± 0.90
21.54_1540_84_N-Acetylglutamic acid19.89 ± 2.4630.50 ± 1.6623.39 ± 5.65
22.42_1588_129_Unknown0.00 ± 0.004.10 ± 0.313.46 ± 0.49
22.92_1616_211_Unknown0.00 ± 0.005.55 ± 1.810.00 ± 0.00
22.94_1617_211_Unknown0.00 ± 0.005.49 ± 1.810.00 ± 0.00
23.49_1647_355_4-Hydroxybenzoic acid4.31 ± 1.170.00 ± 0.003.66 ± 1.76
24.15_1683_103_Unknown4.12 ± 0.450.00 ± 0.000.00 ± 0.00
24.34_1693_116_L-Asparagine0.00 ± 0.001.55 ± 0.461.05 ± 0.50
24.87_1723_275_Unknown0.00 ± 0.002.18 ± 0.361.38 ± 0.56
25.23_1742_217_Mannitol132.1 ± 24.0171.57 ± 21.62166.59 ± 27.34
25.86_1777_174_Ornithine0.00 ± 0.0014.85 ± 2.4710.56 ± 2.45
26.33_1803_156_L-Glutamine0.00 ± 0.0022.68 ± 6.2818.73 ± 5.80
26.44_1809_429_Unknown2.09 ± 0.720.00 ± 0.002.13 ± 1.23
26.8_1829_217_Unknown0.00 ± 0.008.40 ± 2.939.11 ± 2.76
27.07_1843_142_Ornithine0.00 ± 0.0022.96 ± 2.0017.04 ± 3.60
27.13_1847_273_Citric acid53.58 ± 6.0348.34 ± 4.7138.37 ± 6.81
27.36_1859_217_Unknown0.00 ± 0.007.89 ± 1.083.43 ± 1.09
27.75_1881_174_Unknown0.00 ± 0.006.15 ± 1.014.34 ± 0.61
28.03_1896_103_Fructose methoxyamine93.76 ± 24.4172.98 ± 11.60174.17 ± 14.85
28.21_1906_103_Fructose methoxyamine80.99 ± 21.7158.83 ± 10.60159.67 ± 12.53
28.46_1919_319_Glucose methoxyamine84.38 ± 6.42133.13 ± 22.10129.33 ± 13.21
28.78_1937_319_Galactose methoxyamine29.26 ± 3.2667.88 ± 11.3464.22 ± 6.94
28.95_1947_174_L-Lysine0.00 ± 0.0011.48 ± 0.828.91 ± 2.61
29.57_1980_217_Unknown0.00 ± 0.001.75 ± 0.511.45 ± 0.42
29.67_1986_72_Unknown0.00 ± 0.009.22 ± 0.616.24 ± 2.07
29.98_2003_204_Unknown0.00 ± 0.006.09 ± 1.744.52 ± 1.43
30.69_2042_318_myo-Inositol7.40 ± 0.952.10 ± 0.301.59 ± 0.32
31.21_2070_313_Hexadecanoic acid0.00 ± 0.002.74 ± 0.290.00 ± 0.00
31.69_2096_318_myo-Inositol15.58 ± 1.805.33 ± 0.354.11 ± 0.59
33.56_2199_319_Unknown2.01 ± 0.690.00 ± 0.000.00 ± 0.00
34.3_2253_75_Octadecanoic acid0.00 ± 0.003.34 ± 0.462.00 ± 0.84
35.23_2320_387_Glucose-6-phosphate methoxyamine2.56 ± 0.422.00 ± 0.170.65 ± 0.27
35.48_2339_204_Unknown14.59 ± 1.937.27 ± 1.185.07 ± 1.01
37.81_2509_446_Unknown0.00 ± 0.003.31 ± 0.642.42 ± 0.44
38.41_2553_387_Unknown0.00 ± 0.002.87 ± 0.261.87 ± 0.27
40.2_2683_217_Unknown2.09 ± 0.866.44 ± 0.665.98 ± 1.21
40.9_2734_361_Trehalose168.0 ± 37.0106.18 ± 11.6874.90 ± 10.46
41.05_2745_217_Unknown0.00 ± 0.003.39 ± 0.872.52 ± 0.78
41.48_2776_217_Unknown3.11 ± 0.971.10 ± 0.510.00 ± 0.00
41.58_2784_318_Unknown1.96 ± 0.700.00 ± 0.000.00 ± 0.00
42.89_2895_204_Unknown15.08 ± 2.6910.14 ± 1.026.78 ± 1.21
43.67_2963_459_Alternariol3.78 ± 0.9686.32 ± 9.3456.66 ± 10.36
46.78_3237_363_Ergosterol47.58 ± 3.8127.60 ± 4.6021.30 ± 6.40
47.4_3298_?_Unknown3.48 ± 0.821.73 ± 0.350.00 ± 0.00
8.4_?_228_Unknown4.04 ± 1.242.78 ± 1.160.00 ± 0.00
9.78_1079_117_Unknown6.56 ± 1.714.32 ± 1.820.00 ± 0.00

Figure 7. Metabolite profiling of changes in fungal metabolism induced by growth of Stagonospora nodorum on serotonin. (a) Principal component analysis (PCA) score plot of metabolic changes of S. nodorum grown on minimal media (control; squares) and supplemented with 0.3 mM (diamonds) and 1 mM (circles) serotonin. (b) Factor loadings of the 15 compounds with the highest contribution to the variation described by principal component 1 (PC1) labelled by retention time_retention index_name. (c) Representative total ion chromatograms of extracts from S. nodorum grown on minimal media and 1 mM serotonin. The four compounds with the highest influence on PC1 are labelled. The inset shows the absence of alternariol in control samples.

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Central carbon metabolism of S. nodorum is significantly disrupted by MBOA

We found that MBOA significantly inhibited mycelial growth of S. nodorum in vitro and, furthermore, completely inhibited germination of spores at 3 mM. The mechanism behind this inhibition of growth is unknown; therefore we performed GC-MS metabolite profiling to identify changes in primary metabolism induced by MBOA that may cause this phenotype. Metabolites were extracted from wild-type S. nodorum grown for 2 wk on MM containing 0.1, 0.3 and 1 mM MBOA. Subsequent PCA analysis of the data revealed that the samples fell into two distinct clusters separated by PC1, which explained 56% of the variation in the dataset. Group 1 contained the control and 0.1 mM MBOA samples and group 2 contained 0.3 and 1 mM MBOA samples (Fig. S4a). These two groups are consistent with the phenotype observed and the measured radial growth, which showed no significant difference between controls and S. nodorum grown on 0.1 mM MBOA. Factor loadings of PC1 revealed increased abundances of several key primary metabolites (e.g. sucrose, fructose, glucose and mannitol) in the MBOA samples, likely to be indicative of the arrested growth phenotype and the inability to use these glycolytic precursors (Fig. S4b). ANOVA revealed the abundance of 63 of the 234 metabolites detected, which were significantly different (< 0.05) across treatments. Of these, 25 were identified with a match to the NIST08 library or the Golm Metabolome Database and confirmed with in-house databases (Table 2). Approximately two-thirds of the 63 significant metabolites were more abundant in S. nodorum grown in the presence of MBOA.

Table 2. List of fungal metabolites with significant abundance changes in response to 6-methoxy-2-benzoxazolinone (MBOA)
RT_RI_BP_Metabolite IDSignificanceControl ± SE0.1 mM ± SE0.3 mM ± SE1 mM ± SE
  1. The significance of each result is indicated by: *, 0.05 < < 0.01; **, 0.01 < < 0.001; ***, < 0.001. Metabolites are annotated as retention time_retention index_base peak_metabolite ID.

14.53_1247_110_Unknown ** 0.00 ± 0.000.00 ± 0.007.60 ± 2.230.00 ± 0.00
14.72_1255_175_Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.004.81 ± 2.41
15.06_1269_174_Unknown * 8.56 ± 4.4014.03 ± 1.0322.46 ± 5.3920.34 ± 5.56
16.45_1325_191_Unknown * 10.48 ± 5.2515.17 ± 0.5311.82 ± 7.810.00 ± 0.00
20.77_1498_217_Threitol * 0.00 ± 0.000.00 ± 0.008.57 ± 0.7812.69 ± 6.47
20.93_1507_217_Erythritol *** 0.00 ± 0.000.00 ± 0.0013.45 ± 1.640.00 ± 0.00
22.43_1588_129_g-hydroxyglutaric acid * 0.00 ± 0.000.00 ± 0.000.00 ± 0.007.57 ± 3.96
22.85_1611_188_Tryptamine * 0.00 ± 0.000.00 ± 0.000.00 ± 0.003.42 ± 1.73
24.36_1693_116_L-Asparagine * 14.43 ± 10.5831.95 ± 3.9118.08 ± 8.867.14 ± 4.25
24.89_1722_275_Unknown * 0.00 ± 0.006.15 ± 3.092.85 ± 1.430.00 ± 0.00
25.25_1741_217_Arabitol * 86.57 ± 58.04112.22 ± 6.76127.17 ± 23.90165.12 ± 69.20
25.88_1776_174_Ornithin * 0.00 ± 0.000.00 ± 0.004.88 ± 2.530.00 ± 0.00
26.31_1799_287_Unknown ** 0.00 ± 0.0010.60 ± 1.257.63 ± 1.625.47 ± 2.84
26.46_1807_299_Glyceric acid-3-phosphate * 0.00 ± 0.000.00 ± 0.004.19 ± 2.408.38 ± 4.21
27.34_1855_231_Unknown * 0.00 ± 0.004.90 ± 2.470.00 ± 0.000.00 ± 0.00
27.77_1879_174_Unknown * 5.91 ± 2.9611.99 ± 1.648.66 ± 4.380.00 ± 0.00
28.04_1893_103_Fructose methoxyamine ** 55.94 ± 47.6974.92 ± 28.92133.20 ± 28.77178.18 ± 65.99
28.22_1903_103_Fructose methoxyamine ** 45.39 ± 39.0365.45 ± 26.35122.96 ± 27.43167.00 ± 62.15
28.32_1908_331_Allantoin * 0.00 ± 0.0020.83 ± 10.7516.52 ± 6.3216.92 ± 13.13
28.47_1917_205_Galactose methoxyamine ** 92.85 ± 26.14118.34 ± 20.96129.56 ± 44.69172.79 ± 55.89
28.79_1934_319_Unknown * 19.97 ± 12.120.00 ± 0.000.00 ± 0.000.00 ± 0.00
28.8_11935_319_Glucose methoxyamine ** 28.18 ± 14.3351.95 ± 11.0162.74 ± 23.43104.18 ± 37.97
28.99_1945_319_Mannitol * 225.88 ± 56.42242.38 ± 25.01244.83 ± 43.97273.57 ± 79.86
29.3_1978_218_L-Tyrosine ** 0.00 ± 0.0011.74 ± 1.786.16 ± 3.130.00 ± 0.00
29.92_2010_205_Glycerol * 0.00 ± 0.000.00 ± 0.000.00 ± 0.005.59 ± 2.93
30.44_2038_191_Unknown * 0.00 ± 0.000.00 ± 0.004.87 ± 2.490.00 ± 0.00
30.71_2050_318_Myo-inositol * 17.69 ± 4.2420.60 ± 2.7812.91 ± 1.498.38 ± 4.21
31.39_2077_289_Unknown ** 0.00 ± 0.000.00 ± 0.0022.03 ± 8.560.00 ± 0.00
31.71_2128_305_Unknown ** 25.86 ± 4.0435.24 ± 3.5820.69 ± 4.6716.61 ± 6.18
33.59_2203_319_Unknown * 0.00 ± 0.000.00 ± 0.004.21 ± 2.1115.28 ± 7.91
33.9_2239_339_Octadecanoic acid ** 5.91 ± 2.9613.08 ± 2.578.84 ± 2.170.00 ± 0.00
34.22_2247_188_Unknown ** 0.00 ± 0.005.05 ± 0.837.12 ± 2.620.00 ± 0.00
35.02_2314_267_Unknown *** 0.00 ± 0.000.00 ± 0.009.48 ± 0.780.00 ± 0.00
35.09_2332_103_Fructose-6-phosphate methoxyamine * 0.00 ± 0.000.00 ± 0.000.00 ± 0.003.42 ± 1.73
35.5_2344_204_Unknown * 7.92 ± 3.9814.30 ± 4.3919.89 ± 5.5838.35 ± 15.61
35.66_2369_217_Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.002.97 ± 1.49
36.22_2404_205_Unknown * 7.95 ± 4.060.00 ± 0.000.00 ± 0.000.00 ± 0.00
36.22_2409_205_Unknown ** 0.00 ± 0.007.04 ± 0.870.00 ± 0.004.90 ± 2.68
37.63_2501_387_Unknown ** 0.00 ± 0.0010.21 ± 1.529.18 ± 1.447.75 ± 3.88
38.44_2601_387_Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.0013.65 ± 6.90
39.08_2608_204_Unknown * 0.00 ± 0.006.07 ± 3.360.00 ± 0.000.00 ± 0.00
39.58_2674_361_Sucrose * 0.00 ± 0.000.00 ± 0.00123.95 ± 77.39130.49 ± 40.01
39.84_2674_204_Lactose *** 0.00 ± 0.007.62 ± 0.582.85 ± 1.430.00 ± 0.00
40.2_2689_217_Unknown * 0.00 ± 0.000.00 ± 0.0011.01 ± 5.5532.52 ± 19.07
40.92_2769_361_Trehalose ** 206.22 ± 19.17229.21 ± 25.66155.10 ± 61.60196.02 ± 107.61
41.07_2776_217_Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.0014.68 ± 9.24
41.5_2776_217_Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.0025.96 ± 15.46
41.6_2800_318_Unknown * 0.00 ± 0.005.41 ± 3.410.00 ± 0.000.00 ± 0.00
42.92_2966_204_Unknown * 0.00 ± 0.006.94 ± 1.286.82 ± 3.6325.81 ± 14.09
43.72_2982_459_Alternariol * 0.00 ± 0.000.00 ± 0.000.00 ± 0.007.73 ± 4.60
45.39_3162_204_ Unknown * 0.00 ± 0.000.00 ± 0.000.00 ± 0.005.62 ± 3.30
48.06_3363_470_Unknown ** 7.95 ± 4.0630.38 ± 10.240.00 ± 0.000.00 ± 0.00
48.18_3367_361_Raffinose * 0.00 ± 0.000.00 ± 0.0097.02 ± 48.94126.59 ± 51.21
50.77_361_Unknown * 0.00 ± 0.002.86 ± 1.440.00 ± 0.000.00 ± 0.00
51.7_204_Unknown * 0.00 ± 0.000.00 ± 0.005.51 ± 2.989.82 ± 4.92
56.75_217_Unknown * 0.00 ± 0.000.00 ± 0.0021.61 ± 10.8139.56 ± 18.49

The pattern of serotonin accumulation differs in the presence of multiple effector-susceptibility gene interactions

This research has demonstrated that wheat leaves infiltrated with SnToxA produce a number of plant secondary metabolites that inhibit the growth or sporulation of S. nodorum. LC-MS analysis of S. nodorum-infected plants harbouring only the Tsn1 susceptibility gene (cv BG261) demonstrated that concentrations of serotonin in infected leaves were comparable to the concentrations found to inhibit sporulation in vivo. This raises the question of whether this case remains in cultivars with multiple susceptibility genes (cv Grandin) during an infection with S. nodorum wild-type SN15, which produces a range of infectors, including SnToxA, SnTox1, SnTox2 and SnTox3. We performed LC-MS analysis of SnToxA-infiltrated and S. nodorum-infected T. aestivum cv BG261 harbouring only the Tsn1 susceptibility gene. Infiltrated tissue was harvested at 72 hpi while infected tissue was collected 7 dpi, at which time comparable symptoms to SnToxA infiltration had developed. There was no significant difference in the abundance of serotonin in BG261 leaves when either infiltrated with the purified SnToxA protein or infected with the pathogen (the only interaction here SnToxA-Tsn1). In addition to this, we repeated this comparison using the cv Grandin. Unlike BG261, which only harbours Tsn1, Grandin is known to carry multiple dominant sensitivity loci, including Snn2 (SnTox2), Snn3 (SnTox3) and Tsn1 (SnToxA) (Liu et al., 2006, 2009, 2012). As expected, the concentration of serotonin in SnToxA-infiltrated Grandin (SnToxA-Tsn1) was comparable to that detected in the BG261 analyses, showing no difference in accumulation of SnToxA-induced serotonin in these two cultivars (Fig. 8). By contrast, serotonin concentrations were 10-fold lower in Grandin when infected with S. nodorum (multiple effectors, multiple susceptibility genes). Concentrations of feruloylserotonin followed the same trend and were significantly less abundant in infected Grandin and BG261 than in SnToxA-infiltrated wheat (< 0.001; Fig. S5a). Feruloyltryptamine was only present in SnToxA-infiltrated cultivars and completely absent in infected lines (Fig. S5b). Metabolite profiling of SnTox3-infiltrated BG220 (harbouring the SnTox3 susceptibility gene Snn3) revealed no increase in serotonin or the derivatives mentioned over the same time course spanning infiltration to the onset of necrosis.


Figure 8. The abundance of serotonin decreases significantly in the presence of multiple effector–susceptibility gene interactions. Relative abundance of serotonin in various SnToxA-infiltrated or infected wheat (Triticum aestivum) lines. The cv BG261 only contains the Tsn1 susceptibility gene and therefore SnToxATsn1 is the only interaction that can occur in any combination of infiltration or infection. The cv Grandin contains multiple susceptibility genes and therefore multiple interactions occur in an infection of Stagonospora nodorum where multiple effectors are produced. Letters A–C represent levels of significance. Error bars represent the standard error of the mean (= 3).

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  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

SnToxA induces the accumulation of tryptophan-derived plant secondary metabolites

Here we report the novel finding that the effector SnToxA causes an increase in metabolites derived from the tryptophan metabolic pathway of wheat. One pathway branching from tryptophan metabolism that was up-regulated following SnToxA infiltration was the production of serotonin. We discovered an increase in tryptophan, tryptamine, serotonin and the two hydroxamic acid amides (HCAAs) feruloyltryptamine and feruloylserotonin after SnToxA infiltration. Supporting this result are previously published transcriptomic data revealing that several transcripts encoding enzymes involved in serotonin synthesis in plants, including anthranilate synthase, anthranilate phosphoribosyltransferase and tryptophan decarboxylase, are significantly up-regulated upon PtrToxA treatment (Pandelova et al., 2009). Another significant result of this study of SnToxA-infiltrated wheat was the change in abundance of three benzoxazinoid compounds that are also closely associated with tryptophan metabolism, sharing the precursor indole (Fig. 3).

A novel effect of serotonin in the inhibition of sporulation

While the role of serotonin in mammals as a neurotransmitter has been well established (Berger et al., 2009), the role of this monoamine in plants is less well defined. Serotonin has been identified in 42 plant species, including wheat (Roshchina, 2001), and has been reported to have roles in flowering, senescence, plant growth regulation and defence responses; however, these are not well defined (Odjakova & Hadjiivanova, 1997; Roshchina, 2001; Ishihara et al., 2008; Kang et al., 2009). Since the initial detection of serotonin in wheat, there have been no reports describing the presence or activity of serotonin in wheat. Consistent with recent studies, our data showed that SnToxA induces a plant defence response through the accumulation of metabolites typically associated with antimicrobial activity. It was unclear, though, whether the accumulation of serotonin was part of this response, as no significant antimicrobial activity has been previously reported. To determine this, we investigated the effect of serotonin on the growth of S. nodorum and establishment of disease. Serotonin showed no significant inhibition of fungal growth at 3 mM, but strongly inhibited asexual sporulation in vitro and in vivo at concentrations as low as 0.1 mM. Furthermore, microscopy of pycnidia formed in the presence of serotonin revealed pycnidial structures devoid of spores, indicating a disruption in spore formation or maturation. The asexual sporulation of S. nodorum in vitro is identical to asexual sporulation occurring on the leaf (Solomon et al., 2006b,c). Weak activity of serotonin against Aspergillus spp. has been reported; however, concentrations above 10 mM were required for activity (Lass-Flörl et al., 2003). Serotonin had no significant effect on germination of conidia or fungal growth of Bipolaris oryzae at 3 mM (Ishihara et al., 2008). To the best of our knowledge, we report for the first time the significant inhibition of fungal sporulation by serotonin. Based on our research, serotonin meets the accepted criteria of a phytoalexin as defined by a low-molecular-weight, antimicrobial compound which is synthesized by, and accumulates in the plant after, exposure to a microorganism (Paxton, 1981). Therefore, we report the discovery of serotonin as a novel phytoalexin in wheat.

Serotonin interferes with trehalose synthesis and induces mycotoxin production in S. nodorum

Previous studies by our laboratory and collaborators have demonstrated the critical nature of specific primary metabolic pathways for asexual development in S. nodorum (Solomon et al., 2006c; Lowe et al., 2009; Tan et al., 2009b). Consequently, we undertook GC-MS metabolite profiling of S. nodorum grown in the presence of serotonin to characterize the described sporulation defect. Trehalose was the most significant compound that was less abundant in the S. nodorum grown in the presence of serotonin. Interestingly, trehalose has previously been shown to be important for sporulation of S. nodorum (Lowe et al., 2009). The trehalose-deficient trehalose 6-phosphate synthase (tps1) mutant was characterized by poor lesion development, reduced sporulation in vitro and in vivo with indications that pycnidia formation was arrested at an early developmental stage. This resembles the observations made in the current study, where serotonin inhibited spore formation and maturation within the pycnidia. The specific role of trehalose in fungi is not well established, but several reports suggest that trehalose is required for full pathogenicity in addition to coping with heat and oxidative stress (Fillinger et al., 2001; Foster et al., 2003; Doehlemann et al., 2006; Lowe et al., 2009). In the current study, we attempted to complement the sporulation defect with trehalose supplementation, but we found no significant effect (data not shown). It is unknown whether S. nodorum possesses an acid trehalase capable of trehalose import and therefore utilization of exogenous trehalose (Lowe, 2006).

One of the compounds significantly more abundant in S. nodorum grown in the presence of serotonin was the mycotoxin alternariol. Alternariol was first identified in the fungal genus Alternaria (Scott, 2001) and has been associated with oesophageal cancer (Liu et al., 1992) and a number of other mutagenic and toxic activities (Brugger et al., 2006; Lehmann et al., 2006; Pfeiffer et al., 2007). It was first discovered in S. nodorum in a short-chain dehydrogenase (Sch1) mutant, which, interestingly, is defective in asexual sporulation in a similar manner to S. nodorum grown on serotonin (Tan et al., 2009a). The current study strengthens evidence for a connection between this mycotoxin and fungal sporulation. It is well known that an important link exists between secondary metabolism and sporulation in a number of fungi (Adams & Yu, 1998). This link is yet to be established in S. nodorum, but the consistent presence of alternariol in independent sporulation-impaired mutants (Tan et al., 2009b; Ipcho et al., 2010) implies it has a role in asexual development.

The benzoxazinoid metabolites induced by SnToxA have significant antifungal activity against S. nodorum

The current study identified significant changes in the abundance of several benzoxazinoids. Benzoxazinoids are present in a number of cereals, are involved in alleopathy and resistance to insects and have been implicated in defence against various fungal pathogens on numerous occasions over recent decades (Elnaghy & Shaw, 1966; Couture et al., 1971; Long et al., 1978; Bücker & Grambow, 1990). Benzoxazinoids are phytoanticipans stored in the vacuoles of plants in their inactive glucoside form, and upon tissue disruption, contact with β-glucosidases converts these compounds to the biocidal aglycone (Du Fall & Solomon, 2012). These compounds are therefore particularly relevant in defence against necrotrophic pathogens, for which tissue disruption and host cell death are a prerequisite to establishing a successful infection. The current study identified the reduction of DIMBOA-glc and increase in HMDBOA-glc and MBOA after SnToxA infiltration of wheat, suggesting utilization and activation of stored benzoxazinoids as part of a defence response (Grambow et al., 1986; Oikawa et al., 2004). It was also established that MBOA significantly inhibited fungal growth of S. nodorum by 60% at 0.3 mM, and completely inhibited spore germination at 3 mM. Benzoxazinoids have been shown to demonstrate antifungal activity against S. nodorum (Baker & Smith, 1977), but this is the first report to our knowledge of the complete inhibition of spore germination. Quantification of benzoxazinoids in plants has determined the concentrations of these compounds to be 200–500 μg g−1 of FW in wheat and maize seedlings and 0.15 mM in apoplastic fluid extracted from maize (García et al., 1998; Ahmad et al., 2011). The concentrations of MBOA used in the current study of 0.1–3 mM are therefore within a range that the plant is capable of accumulating.

In the current study, no difference was seen in radial growth or metabolism in the S. nodorum control or 0.1 mM MBOA samples; however, the 1 and 3 mM MBOA plates showed significant changes in both respects. This suggests that S. nodorum is capable of detoxifying this compound at concentrations as high as 0.1 mM, but concentrations beyond this become increasingly toxic. Certain fungal isolates, including Gaeumannomyces spp. and Fusarium spp., are capable of detoxifying and therefore tolerating certain concentrations of these benzoxazinoids (Friebe et al., 1998). Consistent with this idea was the brown discoloration of agar surrounding S. nodorum on plates containing MBOA, suggesting compound secretion. A similar discoloration of the media in the presence of MBOA was observed previously with the necrotroph Gaeumannomyces graminis var. tritici (responsible for take-all disease of cereal crops). This pathogen is also capable of detoxifying up to 0.1 mM MBOA before significant growth inhibition (Friebe et al., 1998).

How does S. nodorum survive the plant defence responses induced by SnToxA?

Previous transcriptomic, proteomic and metabolomics data have shown that ToxA induces a wide range of defence responses in the host (Vincent et al., 2011; Pandelova et al., 2012). The current study has contributed further evidence of this response, finding that SnToxA induces a number of plant defence secondary metabolites that inhibit the growth or sporulation of S. nodorum. Furthermore, the current study has shown that MBOA and serotonin exhibit their inhibitory effect on S. nodorum at biologically relevant concentrations. In addition to this direct antimicrobial effect, both benzoxazinoids and serotonin have established roles in plant innate immunity. Benzoxazinoids have been shown to contribute to penetration resistance against Setosphaeria turtica and found to act as signals inducing callose deposition in the plant, a reaction indicative of a plant immune response (Ahmad et al., 2011). Serotonin and related HCAAs undergo polymerization and incorporation into the plant cell wall, forming an effective physical barrier against a number of necrotrophic pathogens (Ishihara et al., 2008; Kanjanaphachoat et al., 2012). Increased susceptibility of a serotonin-deficient rice mutant to B. oryzae was postulated to be a result of the inability of the mutant to form this strengthened barrier (Ishihara et al., 2008; Fujiwara et al., 2010). These results therefore indicate that the tryptophan-derived plant secondary metabolites induced by SnToxA, which have both direct antifungal activity and roles in plant innate immunity, have significant potential as effective mechanisms to control S. nodorum. Additional putatively identified secondary metabolites with confirmed and potential antifungal activities that were also induced by SnToxA infiltration were coumaroylagmatine and caffeoylputrescine (Table S1; Stoessl, 1966; Chen et al., 2006; Zhang et al., 2012). This raises the question of how the pathogen overcomes or survives these defence responses during colonization of the leaf preceding host cell death.

The strong induction of the serotonin pathway and benzoxazinoid synthesis was discovered in a wheat cultivar with only the Tsn1 susceptibility gene and infiltrated with the purified effector SnToxA. We therefore investigated whether these compounds also accumulated in wheat leaves infected by S. nodorum. We utilized a cultivar containing only the SnToxA susceptibility gene Tsn1 (cv BG261) and the cv Grandin, which harbours dominant susceptibility loci to several S. nodorum effectors, including SnToxA, SnTox3 and SnTox2 (Liu et al., 2006, 2009, 2012). In infections with only the Tsn1–SnToxA interaction, serotonin increased in the same manner as in SnToxA-infiltrated leaves. However, a significant 10-fold reduction in the concentrations of serotonin and feruloylserotonin and the complete disappearance of feruloyltryptamine was observed in an infection of Grandin where multiple susceptibility genes were present. In addition to this, SnTox3 infiltration of the sensitive cv BG220 (Snn3) revealed no increase in serotonin or the aforementioned derivatives. Our data suggest that S. nodorum, possibly through the action of other effectors, is able to suppress the serotonin defence pathway induced by SnToxA.

While the idea of suppression of plant defences somewhat conflicts with the currently accepted notion of necrotrophs exploiting the cell death that occurs as a result of this response, it is likely that a fine balance exists in order for the pathogen to survive the initial stages of infection while exposed to plant metabolites with antifungal activity, as demonstrated in the current study. The effector Victorin-LOV1 susceptibility gene system in A. thaliana is a known example of a necrotrophic effector that suppresses local plant defence responses (Lorang et al., 2012). Victorin binds and inhibits the activity of its thioredoxin target TRX-5 h, which controls the redox state of the transcription regulator of systemic acquired resistance, called NPR1. Hence, Victorin inhibits activation of NPR1 activity and significantly suppresses local plant defence responses. It is well documented that pathogens are constantly evolving mechanisms to overcome stages of ETI in the plant, with additional pathogen effectors again leading to ETS (Jones & Dangl, 2006). Further research is necessary to establish how necrotrophs survive the initial stages of plant infection and if they indeed actively suppress aspects of plant defence during this stage.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

This study was kindly supported by the Australian Grains Research and Development Corporation (GRDC). P.S.S. is an Australian Research Council (ARC) Future Fellow and L.A.D.F. thanks the ARC and GRDC for postgraduate scholarships. The authors gratefully acknowledge the ANU Research School of Biology Mass Spectrometry Facility and also Prof. Adrienne Hardham for her assistance with the microscopy experiments.


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  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.


Fig. S1 Extracted compound chromatogram (ECC) of representative wheat extract analysed in positive mode electrospray ionization (ESI) using LC-MS.

Fig. S2 Relative abundance of serotonin in detached leaves with exogenously applied serotonin is comparable to the concentrations present in infected tissue.

Fig. S3 Detached leaf assays demonstrate no significant effect of MBOA on sporulation of S. nodorum in vivo.

Fig. S4 Metabolite profiling of S. nodorum grown in the presence of MBOA.

Fig. S5 The abundance of serotonin derivatives differ in the presence of multiple effector–susceptibility gene interactions.

nph12356-sup-0002-TableS1.xlsxapplication/msexcel77KTable S1 LC-MS profiling of metabolite changes induced by SnToxA infiltration