Metabolomics links induced responses to the wheat pathogen – tan spot – (Pyrenophora tritici-repentis) to the biosynthesis of flavonoids and bioenergetic metabolism

doi:10.21203/rs.3.rs-3105957/v1

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Research Article

Metabolomics links induced responses to the wheat pathogen – tan spot – (Pyrenophora tritici-repentis) to the biosynthesis of flavonoids and bioenergetic metabolism

https://doi.org/10.21203/rs.3.rs-3105957/v1

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published 31 Jan, 2024

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Introduction

Tan Spot disease of wheat is caused by Pyrenophora tritici-repentis (Ptr), and most of the yield loss is attributed to diseased flag leaves. Few sources of Ptr-resistant germplasm have been established so the responses of wheat to Ptr require further elucidation.

Objectives

The study aimed to characterise the flag-leaf metabolomes of two spring wheat cultivars (Triticum aestivum L. cv. PF 080719 [PF] and cv. Fundacep Horizonte [FH]) in response to Pyrenophora tritici-repentis infection to gain insights into pathogenicity and defence mechanisms associated with the Tan Spot disease.

Methods

PF and FH plants were inoculated with a Ptr strain that produces the necrotrophic toxin ToxA. The metabolic scenarios in flag leaves prior to (0 h) and during (24, 48, 72, and 96 h post-inoculation [hpi]) challenge with Ptr were investigated using untargeted flow infusion ionisation – high resolution mass spectroscopy (FIE-HRMS).

Results

At the flag-leaf stage, both cultivars displayed susceptibility to Ptr. Comparisons of Ptr- and mock-inoculated plants indicate a major metabolic shift occurred at 24 hpi in FH, and at 48 hpi in PF. In PF, there was a significant accumulation of flavonoids, phenylpropanoids, and glucosinolates. Pathway enrichment analysis suggested that Ptr-triggered alterations in chloroplast and photosynthetic machinery in both cultivars, especially in FH at 96 hpi. In PF, alterations in sugar metabolism as well as in glycolysis and glucogenesis pathways were observed. In a wheat-Ptr interactome in integrative network analysis, the pathways “flavone and flavonol biosynthesis” and “starch and sucrose metabolism” were the key metabolic processes underlying PF-FH-Ptr interactions.

Conclusion

These observations suggest the potential importance of flavone and flavonol biosynthesis as well as bioenergetic shifts in susceptibility to Ptr. Further, they highlight the applicability of metabolomics to provide novel insights into wheat pathosystems.

Tan Spot disease

Pyrenophora tritici-repentis

yellow spot

metabolism

flow-infusion electrospray mass spectrometry

The Tan Spot (TS) disease of wheat (syn. yellow spot), caused by Pyrenophora tritici-repentis (Ptr), can lead to yield losses of up to 60% (Rees & Platz, 1989). Most of the yield loss is attributed to TS severity in the flag leaves, which in turn results in grain size reduction (Bhathal et al., 2003; Rees & Platz, 1989). Ptr is a necrotrophic pathogen where different strain can produce one or more phytotoxic effectors, (ToxA, ToxB, and ToxC,) that induce necrosis and chlorosis in sensitive lines (Effertz et al., 2002; Sarma et al., 2005; Strelkov et al., 1999). Ptr strains may be classified into different races based on the production, either singly or in combination, of these toxins (Lamari et al., 1995). Worldwide, ToxA-producing Ptr races are the most common (Abdullah et al., 2017; Ali & Francl, 2003; Gamba et al., 2012; Kokhmetova et al., 2020; Lamari et al., 2003). ToxA is a proteinaceous necrotising effector that is internalised to chloroplasts via the binding protein ToxABP1 (Manning et al., 2007, 2008). Biochemical and transcriptomic assays have shown that ToxA treatment compromised photosynthes, confer sensitivity to Ptr ToxC and Ptr ToxB, have also been mapped (Abeysekara et al., 2010; Effertz et al., 2002; Friesen & Faris, 2004). Some quantitative trait loci (QTL) for TS resistance have been defined and designated Tsr (Tan Spot resistance) (Anderson et al., 1999; Chu et al., 2008, 2010; Faris et al., 1996, 2013; Faris & Friesen, 2005; Gamba & Lamari, 1999; Hu et al., 2019; Li et al., 2011; Singh et al., 2008, 2010, 2016; Tadesse et al., 2006b, 2006a). Eight major Tsr genes (Tsrl, Tsr2, Tsr3, Tsr4, Tsr5, Tsr6, TsrHar, and TsrAri) have been mapped to different points on the wheat genome (McIntosh et al., 2013). However, the underlying mechanism(s) by which these protein interactions systemically affect TS disease development and wheat metabolism remains to be defined.

A Brazilian-based wheat breeding programme has derived Fundacep Horizonte (FH) (Cooperativa Central Gaúcha Ltda Tecnologia/FUNDACEP) and PF 080719 (PF) which has been targeted following field assessments as showing differential susceptibility to Tan Spot, (Cunha et al., 2016). FH is a high-yielding spring wheat cultivar widely used in wheat-producing areas in southern Brazil. PF is a highly productive lineage developed at the Embrapa wheat breeding programme with significant resistance to wheat leaf rust, powdery mildew, and leaf blotches. To accelerate the exploitation of potential sources of resistance into elite genotypes, we exploited the post-genomic potential of metabolomics to characterise the responses in PF compared to FH. Recent advancements in mass spectrometry technologies have enabled high throughput quantitative and qualitative assessments of metabolic compounds, including from plants and microorganisms (Allwood et al., 2021). Metabolomics has then become an important technology for plant breeders and phytopathologists, especially when integrated with other systems biology tools (Aliferis et al., 2014; Kumar et al., 2017; Rosato et al., 2018; Weckwerth 2003). The metabolomics of wheat-Ptr interactions have been characterised in seedlings (Ferreira et al., unpublished data) but it has yet to be applied to adult growth stages.

The emergence of the flag leaf is an especially important stage being unambiguously linked to grain development/yield in several cultivars (Carmo-Silva et al., 2017; Khaliq et al., 2008; Wazziki et al., 2015). This reflects the flag leaf’s role in providing carbon assimilates to the grain as it develops (Evans & Rawson, 1970). Photosynthesis in the flag leaf is affected by genetic background, fertilizers as well as abiotic and biotic stresses (Evans, 1983; Guóth et al., 2009; Inoue et al., 2004; Wazziki et al., 2015). Therefore, the ability to sustain carbon assimilation activity during such as Ptr infection would be a valuable trait for wheat breeding (Araus et al., 2002; Carmo-Silva et al., 2017; Yang & Luo, 2021).

In this study, we demonstrate phenotypic and metabolic changes occurring in FH and PF when challenged with P. tritici-repentis. Focusing on flag leaves, our untargeted metabolomics approach allowed the detection of key metabolites and pathways underlying the responses of each cultivar to Ptr infection as well as the genotypic differences between them.

3.1 Host classifications based on lesion scores.

Seeds from Triticum aestivum L. cv. PF 080719 (PF) and cv. Fundacep Horizonte (FH) were sown in John Innes No2 compost and grown in controlled-environment growth chambers (Conviron, UK) under a 16 h light period at 21 ºC and 8 h of darkness at 18 ºC. The cultivars were subjected to mock-inoculation (M) and Ptr inoculum (I) treatments. Each treatment was applied to three replicates, each being composed of a singular three-plant pot that was randomly distributed within the growth chamber. The treatments were applied at either of two growth stages: GS13 (three-leaf seedlings) and GS65 (flowering/anthesis).

3.2 P. tritici-repentins infections

P. tritici-repentis strains used in this study were isolated from naturally infected wheat fields in southern Brazil. Mycelial plugs of Ptr cultures were transferred to Petri dishes containing V8 media (agar = 15 g; CaCO₃ = 3 g; V8-Juice = 150 mL, dH₂O = 850 mL). The plates were sealed and incubated upside-down for five days under continuous darkness at 25 ºC. The method described by Lamari and Bernier(1989) was followed to stimulate conidiophore formation and optimal conidia production, The plates were flooded with sterilised ultrapure water and the mycelia were flattened with the bottom of a sterile test tube. The resulting solution was discarded, and the plates were incubated at 25 ºC for 24 h of constant light followed by 24 h of darkness at 15 ºC. The mycelia were displaced using a paintbrush and a solution of ultrapure water with 0.5 % (v/) Tween 80. The mixture was then sieved and vortexed to aid conidial detachment. The spores were counted in nine aliquots and the final concentration was adjusted to 3000 conidia / mL^− 1. Inocula were sprayed over all leaves until run-off. Inoculated plants were incubated in a humidity chamber for 24 h. Mock-inoculated plants received the treatment of sterile ultrapure water with 0.5 % (v/) Tween 80 and were incubated as mentioned above but in separate chambers. Lesion phenotypes were scored as R = resistant; MR = moderately resistant; MRMS = moderately resistant to moderately susceptible; MS = moderately susceptible; S = susceptible.

3.3 Metabolite extractions

Disks were punched out from fully extended flag leaves from each genotype at 0, 24, 48, 72, 96, and 168 h post-inoculation with the Ptr strain BR154, composing four/five biological replicates. The samples were immediately frozen in liquid nitrogen and stored at -80°C until processed. Approximately 40 mg (± 1 mg) of leaf tissue was placed in 2 mL sterile microcentrifuge tubes, each containing one acetone-cleaned stainless-steel bead. The samples were flash-frozen in liquid N₂ and homogenised using a ball mill system. Then, 1 mL of chloroform: methanol: dH₂O (1: 2.5: 1) solution was added to each sample, followed by incubation in a shaker at 4 ºC for 15 minutes. The samples were centrifuged at 5000 × g for 5 min and the supernatant was carefully transferred to a new microcentrifuge tube. An aliquot of 100 µL of each sample was placed into a glass vial and sealed. Quality control samples were prepared by pooling 10 µL of each sample. Untargeted metabolite fingerprinting was performed by Flow Infusion Electrospray Ionization High-Resolution Mass Spectrometry (FIE-HRMS), where the mass-to-charge ratio features (m/z) was generated in negative and positive ionisation modes using a Q Exactivd hybrid quadrupole- Orbitrap mass spectrometer (Thermo-Scientific, UK). Two 20 µL FIE injections were performed in each sample.

3.4 Metabolomic data processing and analysis

The peak data was filtered based on the relative standard deviation of 0.5 and a minimum occupancy of 2/3 in each class using the R package metabolyseR version 0.14.6. The m/z data was normalised based on total ion count (TIC) normalisation and visualised using unsupervised principal component analysis (PCA). Differentially accumulated metabolites (DAMs) were identified in each genotype based on pairwise comparisons between mock- and Ptr-inoculated samples within each time point, designated as PF24, PF48, PF72, PF96, FH24, FH48, FH72, and FH96. Samples collected before treatment were compared between both genotypes, designated as PF/FH. Two-sided Welch t-tests were performed in all these comparisons and the features with adjusted P-values < 0.05 after Bonferroni correction were considered significant DAMs. The peak intensity data was summarised by mean in each subset of genotype/treatment/hpi after log₂(x + 1) transformation. Fold changes were then calculated as the difference between inoculated (I) and mocks (M) over the mock-treated samples, e.g. (I-M)/M.

Molecular formulas were assigned to the explanatory features using the R package MFassign v0.7.7. Functional enrichment was performed by mapping the assigned features to KEGG metabolic network using hypergeometric, PageRank and Diffusion methods within the FELLA package (Kanehisa et al., 2012; Picart-Armada et al., 2018), using Aegilops tauschii as reference. To extract a significant sub-network, the diffusion method was used to score the enriched nodes. After normalisation through z-scores, the nodes with P-value < 0.05 were selected. The resulting graph was imported to Cytoscape v 3.8.2 (Shannon et al., 2003) to construct a representative visualisation.

4.1 Phenotypic characterisation of Ptr interactions with FH and PF

We first evaluated responses to Ptr challenge in FH and PF at two different developmental stages; the seedling stage (GS13) and during flowering when the flag leaf had fully emerged (GS65). Plants were inoculated with three ToxA positive strains with varying levels of virulence (BR13 = mild; BR154 = moderate virulence; BR29 = highly virulent), in two independent experiments. Scoring for disease severity indicated that response in PF widely varied from resistant to susceptible dependent on the Ptr virulence and growth stage (Table 1).

Table 1. Classification of disease scores against P. tritici-repentis infection in the wheat lines PF 080719 (PF) and Fundacep Horizonte (FH) at different growth stages.

R = resistant; MR = moderately resistant; MRMS = moderately resistant to moderately susceptible; MS = moderately susceptible; S = susceptible.

*Based on mean scores from assessments at 336 hours post inoculation (hpi).

At the seedling stage PF exhibited a resistant phenotype when challenged with the BR13 but was only moderated tolerant to moderately susceptible (MRMS to MS) to BR154 and BR29. At the adult stage, PF plants were highly susceptible to BR29. FH seedlings exhibited MRMS to MS phenotypes, but adult plants were clearly susceptible to all strains of Ptr. Considering TS symptom development in seedlings, PF had exhibited minimal chlorosis following interaction with Ptr by 168 hpi (Fig. 1). At the adult stage, chlorosis was observed in PF from 120 hpi. In FH, both chlorosis and lesion coalescence were observed from 120 hpi at either growth stage (Fig. 1).

4.2 Metabolomic characterisation of Ptr interactions with FH and PF

Given the importance of the wheat flag leaf, metabolomic assessments focused on that stage. Flag leaf samples were collected from FH and PF at 0, 24-, 48-, 72-, and 96-hours post inoculation (hpi) with Ptr strain BR154. Samples were extracted and profiled using FIE-HRMS. The derived data were assessed by PCA. The main source of variation across the first principal component (PC1), accounting for 18.43% of the variance, was genotype (FH vs PF) (Fig. 2a). There was no significant discrimination between mock and inoculated samples when all time points were considered together. When different time points were considered, the mock-inoculated samples exhibited greater variations compared to inoculated samples in both genotypes (Fig. 2b).

Given the minor metabolomic changes seen with Ptr challenge, pairwise comparisons between inoculated (I) versus mock-inoculated (M) samples from each line in each time point were performed using Welch's two-sided t-test to identify differentially accumulated mass ions (DAMs). A total of 1002 DAMs were identified (Fig. 3; Table S1).

The significant differences seen between PF and FH at 0 h (206 DAMs) were taken as indicating genotypic differences. Following infection, FH has most DAMs at 24 hpi, which accounted for 72% of the total for this genotype. Conversely, PF showed most DAMs at 48 hpi compared to the M samples (Fig. 3a). Most of the explanatory features underlying genotypic differences between PF and FH were present in higher concentrations in PF, with log₂ fold changes up to 34.85 (Table S1). Of these DAMs, approximately 80% are products from the negative ionisation reaction and only 8.25% could be annotated. Considering the annotated DAMs, the genotypic differences were reflected in different levels of hippurate, diacetyl, acetone cyanohydrin, and the flavonoids dihydrophaseic acid (DPA), desulfoglucotropeolin (UGT4B1), 3,7-Di-O-methylquercetin, apigenin 7-O-[beta-D-apiosyl-(1->2)-beta-D-glucoside], and 1-O-vanilloyl-beta-D-glucose (Table S1). Three mass ions annotated as the flavonoids apigenin 7-O-neohesperidoside (m/z = 615.14032, negative mode) and vitexin (m/z = 431.09912 and 432.1022, negative mode) were the only annotated compounds found in greater concentration in FH than in PF (log₂FC between 0.75 and 0.89).

We next considered how many of the Ptr-induced changes were specific to each genotype. As shown in the Venn diagram, only 56 DAMs were shared by PF and FH in their responses to Ptr (Fig. 3b, Table 2). Of these, Ptr infection induced (i) significant increases in the concentration of 53 metabolites, (ii) the downregulation of one compound, and (iii) opposing accumulation in PF and FH of two compounds (Table 2). The m/z p337.8598 with a predicted molecular formula C9H2NO3P3S was the only compound downregulated in both lines; in FH at 24 hpi and in PF at 48 hpi (Table 2). Phosphatidylinositol diphosphate (m/z = 339.8223, negative mode [M-3H]³⁻), exhibited a × 11.5-fold increase in inoculated flag leaves from FH at 24 hpi compared the controls, but it was significantly downregulated in PF at 96 hpi (fold change of × 1.66). Conversely, the metabolite with the predicted molecular formula C15H16N2O3 (m/z = 274.12589, positive mode), which could not be identified, showed significant increases in PF at 72 and 96 hpi but was suppressed as 24 hpi in FH compared to controls (Table 2).

Table 2

Common differentially accumulated metabolites found in pairwise comparisons between Ptr-inoculated and control samples from FH and PF at 24, 48, 72, and 96 h post-inoculation. Numbered cells represent the relative log₂ fold change values from each significant comparison, whereas blank cells are non-significant interactions.
m/z*	FH24	FH48	FH96	PF24	PF48	PF72	PF96	MF	Isotope	Adduct
p359.12054	2.07				12.30			C14H20N3O6P	13C	[M + H]1+
p419.1235			8.71		11.17			C16H26O10	13C2	[M + K]1+
n805.33246	4.89				7.64			C37H60O15P2		[M-H]1-
n129.0379	10.84				6.95			C6H9OP	13C2	[M-H]1-
p325.03995	2.94				6.60			C16H15O3P		[M + K]1+
n598.3089	11.79				6.21			C28H50O11	13C	[M + Cl]1-
p338.1908			4.14		6.02			C14H29N2O5P	13C	[M + H]1+
n789.31079		5.39			5.72			C36H55O17P		[M-H]1-
p543.24054	2.35				4.80			C23H38N2O11	18O	[M + Na]1+
p316.09979	2.73				4.65			C9H13N7O6		[M + H]1+
p835.40015		9.69			4.22			C38H60NO16P		[M + NH4]1+
n433.19092	9.65				4.00			C21H32O7	13C2	[M + Cl]1-
p196.05597	2.33				3.97			C5H11N2O4P	13C	[M + H]1+
p442.1196			14.13		3.96
p302.10339	2.30			1.03	3.94			C15H13N3O4	13C2	[M + H]1+
p143.07237	3.28				3.90
p410.24127			3.06		3.63			C19H31N5O5		[M + H]1+
p295.168		7.91	3.18		3.56			C12H25N2O4P	13C2	[M + H]1+
p197.05678	2.47				3.55			C5H11N2O4P	18O	[M + H]1+
p373.06198	4.64				3.35
p210.07156	13.17				3.18
p409.22824			3.85		2.96	0.90		C16H32N4O8		[M + H]1+
p346.04242	2.13				2.95
n518.18787	5.55				2.85			C22H33NO13		[M-H]1-
p394.24567		4.55			2.77			C17H36N3O5P		[M + H]1+
n535.2688			4.16		2.74			C26H44O9		[M + Cl]1-
p614.22638			1.86		2.73
n640.11554		8.35			2.59			C26H27NO18		[M-H]1-
n569.29651			4.77		2.56			C34H44O5	13C2	[M + Cl]1-
p178.04535	8.71				2.55
p212.03348	1.71				2.39			C5H8NO3PS	13C	[M + NH4]1+
n114.03838			2.83		2.36			C5H8NP	13C2	[M-H]1-
p395.24908		5.52			2.30			C17H36N3O5P	13C	[M + H]1+
n525.30792			3.02		2.07			C28H46O9		[M-H]1-
p300.14139	2.48				2.05			C9H21N3O8		[M + H]1+
p391.21811			1.56		1.97			C19H33N3O3	13C	[M + K]1+
p497.14703			1.74		1.88			C23H30O8P2		[M + H]1+
n432.18832	2.95				1.82			C21H32O7	13C	[M + Cl]1-
n280.02603	3.30				1.76
p440.25189			9.71		1.71			C43H74O18		[M + 2H]2+
p201.07129	5.07				1.70
n420.15143	5.05				1.69
p308.18015		3.08			1.66			C14H24O4S	13C2	[M + NH4]1+
p132.08009	1.19				1.56
p171.06078	2.03				1.49			C3H10N2O6		[M + H]1+
p111.03996	1.69				1.49
p177.05382			2.32		1.41			C5H13O3P	18O	[M + Na]1+
p130.04926	1.41				0.86			C6H9OP	13C	[M + H]1+
p137.05547	0.89				0.76			C4H10NO2P	13C	[M + H]1+
n551.19818		5.64			0.72			C25H40O7P2	18O	[M + Cl]1-
p326.05203			1.66		0.60			C7H18N3O7P		[M + K]1+
p168.04987	1.45				0.51			C9H20NO10P	13C	[M + 2H]2+
p337.8598	-0.60				-0.83			C9H2NO3P3S		[M + K41]1+
n339.8223	3.53						-0.73
n467.15039		0.90				1.71		C20H30O10		[M + Cl37]1-
p274.12589	-0.62					1.31	2.77	C15H16N2O3	13C	[M + H]1+

*The prefix letters indicate the ionisation modes: n = negative, p = positive.

Considering the specific changes occurring in each cultivar, FH had more DAMs that increased in concentration at all time points following Ptr infection than compounds that were suppressed by the pathogen whereas there was a greater number of downregulated compounds in PF at 24 and 96 hpi (Fig. 3c). Overall, Ptr induced small decreases in the levels of metabolites, with the greatest downregulation observed in FH at 72 hpi (log₂FC of -0.98; m/z = n408.07086 [C16H21NO9]) and in PF at 48 hpi (log₂FC of -0.94; m/z = n326.08875). The m/z = 518.138 (negative mode) significant in PF at 72 hpi, had the overall greatest accumulation in response to Ptr, with log₂ fold change of 119.46. When investigating the DAMs regarding their mass-to-charge ratios, the higher frequency of DAMs with m/z lower than 400 was notable in FH at 24 hpi (Fig. 3c).

We next examined the annotated DAMs found in FH responses to Ptr infection. At 24 hpi, Ptr induced increases in S,S-dimethyl-beta-propiothetin, piperideine, umbelliferone, phytosphingosine, and the downregulation of anthranilate, L-valine, and N-methylethanolamine phosphate. At 72 hpi, the flavonoids quercitrin, vitexin, and vitexin 2''-O-beta-D-glucoside were significantly accumulated in flag leaves infected with Ptr. At 96 hpi, there was the downregulation of 6-deoxydihydrokalafungin (DDHK), an intermediate in the biosynthesis of type II polyketide products. In PF at 48 hpi, there was an accumulation of flavonoids, phenylpropanoids, and glucosinolates. These included 1-O-sinapoyl-beta-D-glucose, coumarin, D-glucosaminate, xanthohumol, DDHK, L-homomethionine, 3-O-alpha-mycarosylerythronolide B and L-valine (Table S1). This time point also included significant decreases in sucrose in the infected flag leaves.

4.3 Insights into responses to Ptr through pathway enrichment and network analysis

To provide an overview of key metabolic changes occurring in either genotype following Ptr infection, pathway enrichment, and integrative network analyses were performed. The explanatory features identified by our pairwise statistical analysis were mapped to KEGG ids and screened against the KEGG network using PageRank, hypergeometric, and diffusion algorithms. Over-representation analysis, performed via hypergeometric test, targeted seven significantly enriched pathways: “flavone and flavonol biosynthesis” (PF/FH; FH72), “galactose metabolism” (PF48), “starch and sucrose metabolism” (PF48), “ABC transporters” (PF48), and “glycosphingolipid biosynthesis” (PF48), (Table S2). PageRank and diffusion models also targeted these pathways, except for “sphingolipid metabolism” which the hypergeometric test suggested was enriched in PF48 (Table S2).

Using a diffusion model to visualise our data (Picart-Armada et al., 2017), we observed that each genotype/hpi showed enrichment in a discrete set of metabolic processes except for “flavone and flavonol biosynthesis” (Fig. 4). This aligned with the specific metabolic changes induced in each genotype to Ptr infection. At 24 hpi, FH was enriched in the reductive acetyl-CoA pathway, NAD biosynthesis, and tryptophan, sulphur, and one-carbon (C1 unit) metabolism. No significant pathways were enriched in FH at 48 hpi, but at 72 hpi there were shifts in N- and O-glycosylation related pathways and in the biosynthesis of flavonoids. At 96 hpi, Ptr infection affected biosynthetic pathways of secondary metabolites, tocopherol, menaquinone, ubiquinone, phylloquinone, and plastoquinone. These are components of photosynthetic processes that could be occurring as a result of chloroplast targeting by toxins. In PF, uridine and pyrimidine metabolism as well as pantothenate and CoA biosynthesis were prominent at 24 hpi. By 48 h, the metabolic changes were linked to the enrichment of keratan sulfate, glycosphingolipids, nucleotide sugar, trehalose glycogen, starch, sucrose, galactose, and other glycans, metabolism as well as glycolysis and glucogenesis pathways.

To evaluate the metabolic changes in wheat plants induced by Ptr infection, an integrative network was constructed using the diffusion enrichment scores. Our network analysis resulted in a sub-graph populated with two key pathways: “flavone and flavonol biosynthesis” and “starch and sucrose metabolism” (Fig. 5). These two pathways are linked via D-glucose, UDP-glucose, UDP, UDP-L-rhamnose. Importantly, the photoassimilate sucrose connects both pathways. This links photosynthetic and bioenergetic changes in the responses of PF and FH to Ptr. Furthermore, the network analysis points to three enzymes involved in glycosyltransferase activity (galactinol-sucrose galactosyltransferase, flavonol-3-O-glucoside L-rhamnosyltransferase, inositol 3-alpha-galactosyltransferase) and one involved in hydrolase activity, acting on glycosyl bonds (alpha,alpha-trehalase). These proteins could be key roles in the metabolic changes triggered by Ptr.

In this study, we have dissected responses of two wheat genotypes of relevance to Brazilian plant breeding programmes to Ptr infection using an untargeted metabolomics approach. At the flag leaf stage, both PF and FH proved to be susceptible to Ptr, although PF appeared to show some moderate resistance at the seedling stage. Although many facets of the wheat-Ptr pathosystem are well established, the metabolism underlying disease development is understudied. Due to the impact on yield from TS disease pressure on plants at reproductive stages (Bhathal et al., 2003), we focused on a metabolomic assessments of responses in flag leaves.

During plant-pathogen interactions, there is a dual aspect of metabolic shifts where one enhances plant immunity and the other is a response induced by the pathogen to aid infection (Allwood et al., 2010). Although the disease components are generally specific to a pathosystem, phytopathogens such as Magnaporthe grisea are able to induce identical metabolic responses in rice, barley, and Brachypodium distachyon (Parker et al., 2009). Contrary to this, we observed only 7.9% of responses that were common to both wheat cultivars (Fig. 3b). The DAMs that were upregulated (53) and downregulated (1) in both cultivars in response to Ptr infection represent a set of metabolites that could be metabolites linked to susceptibility to Ptr. Thus, although further interpretation of these results was limited by lack of annotation, these serve as candidate compounds for targeted metabolic profiling focused identify traits linked to TS susceptibility.

5.1 Untangling Tan Spot disease metabolism

We identified the significant accumulation of compounds in the classes of flavonoids (quercitrin, vitexin, and vitexin 2''-O-beta-D-glucoside), and coumarin (umbelliferone) in flag leaves of the cultivar FH following challenge with Ptr (Table S1). Secondary metabolites derived from the phenylpropanoid pathway, such as coumarins and flavonoids, have well-established roles in the production of, for example, defensive phytoalexins (Dixon & Paiva, 1995). In wheat, enhanced defences have been attributed to the antioxidant properties of phenylpropanoid and flavonoid compounds (Gunnaiah & Kushalappa, 2014). More specifically, these classes of compounds have been linked to resistance to Fusarium spp. (Chrpová et al., 2021). For instance, vitexin and quercetin, among other flavonoid compounds, significantly increased with the inoculation of Fusarium culmorum in wheat (Buśko et al., 2014). However, our previous transcriptomic-based network analysis indicated the activation of the phenylpropanoid pathway, in means of overexpression of PAL and CHS, to be associated with failed defences of wheat to Ptr (Ferreira et al., 2022). Similarly, in this study, the accumulation of key flavonoid compounds and umbelliferone was ineffective in controlling Ptr. It may be that the ineffectiveness of phenylpropanoids in FH is a consequence of the lack of resistant factors. This is based on the premise that TS resistance relies on the activation of at least two distinct defence mechanisms and a lack of susceptibility factors (Ferreira et al., unpublished data), as well as encoding relevant resistance genes (Faris et al., 2013). This would imply that phenylpropanoids/flavonoid production is most effective only when part of a wider defence response. Thus, when occurring as partial responses, phenylpropanoids/flavonoid defences, could be overcome by Ptr. A similar phenomenon could be observed with the tryptophan biosynthetic pathway which was significantly enriched in FH. Tryptophan is a precursor of phytoalexins, alkaloids, glucosinolates, and auxins (Radwanski & Last, 1995). Although auxins, associated with TS susceptibility (Ferreira et al., unpublished data), were not detected in this metabolomics analysis, the toxic alkaloid piperideine (Matsuura & Fett-Neto, 2015) significantly accumulated in FH at 24 hpi. We hypothesise that Ptr may be able to overcome the antimicrobial properties of these compounds. Further in vitro analyses would inform on the toxicity of piperideine and phenylpropanoids towards Ptr.

The enrichment analysis of KEGG pathways also showed significant changes in the biosynthesis of tocopherol/tocotrienol, phylloquinone, plastoquinone (PQ), ubiquinone (UQ), and other terpenoid-quinone in FH at 96 h post Ptr infection (Fig. 4). Tocopherol (vitamin E) and phylloquinone (vitamin K1) are fat-soluble vitamins, along with PQ, these are chloroplast located and play essential roles in such as photosynthesis, electron transportation, antioxidation, and membrane stability (Havaux, 2020; Munné-Bosch & Alegre, 2010; Swiezewska, 2004). The wheat plants in this study were challenged with a ToxA-producing strain of Ptr, so these results likely reflect the effects of this toxin on chloroplasts. In addition, the enrichment of one-carbon (C1) metabolism, biosynthesis of NAD, ubiquinone (UQ), and other terpenoid-quinone was also identified in FH. The one-carbon metabolism takes place in the cytosol, peroxisomes, mitochondria, and chloroplast, whereas NAD and UQ pathways are mostly in the mitochondria (Gakière et al., 2018; Hanson and Roje 2001; Liu and Lu 2016). Due to the intimate interplay between mitochondria and chloroplasts (Yoshida & Noguchi, 2011), these pathways likely represent the effects of Ptr toxins.

The cultivar PF showed significant changes in the biosynthesis and metabolism of pyrimidine by 24 h of challenge with Ptr (Fig. 4). Pyrimidines are key structural molecules involved in the synthesis of DNA, RNA, lipids, carbohydrates, and glycoproteins (Kafer et al., 2004). Uridine monophosphate is the first pyrimidine in the de novo synthesis pathway, which this and other pathways associated with salvage, phosphotransfer, carbohydrate metabolism, and degradation of pyrimidines occur in the chloroplasts and cytosol (Zrenner et al., 2006). Alterations of biosynthesis and metabolism of pyrimidine pathways have been demonstrated to be an early signalling for programmed cell death (PCD) (Stasolla et al., 2004). In this case, PF early responses to chloroplast perturbations provoked by ToxA could have led to PCD, which in turn, would have facilitated Ptr infection. Pyrimidine nucleotides are vastly involved in the metabolism of sugars (Kafer et al., 2004). Inhibition of de novo synthesis of pyrimidine resulted in stimulation of the compensatory salvage pathway which has been linked to increased levels of uridine nucleotides, the formation of starch from sucrose as well as cell wall synthesis (Geigenberger et al., 2005). Therefore, the enrichment of galactose, trehalose, starch, and sucrose metabolism pathways seen in PF at 48 hpi could be a repercussion of the alterations in pyrimidines at 24 hpi. Sugars also play wider roles in host responses to phytopathogens (Morkunas & Ratajczak, 2014). For instance, trehalose partially induces resistance of wheat to powdery mildew (Blumeria graminis f.sp. tritici) (Reignault et al., 2001), also regulates key biological processes, including starch degradation and stomatal conductance (Figueroa & Lunn, 2016). As also seen in PF, Fusarium graminearum infections of wheat also caused a decrease in sucrose levels (Guenther et al., 2009; Hadinezhad and Miller 2019). As sucrose is the primary photoassimilate in wheat (Takahashi et al., 1998), its reduction is indirect evidence of injuries in the photosynthetic machinery caused by Ptr. Furthermore, lower sugar content in flag leaves will likely have a negative effect on yield (Xu-Dong et al., 2003).

Here we present the set of metabolomic changes occurring in flag leaves during P. tritici repentis infection. Despite both cultivars have displayed a similar degree of TS susceptibility at the reproductive growth stage, the defence responses at the metabolic level were unique to each cultivar. From our results, we suggest that responses that may be commonly associated with resistance, specifically constitutive or biosynthesised flavonoid compounds, are part of failed defence mechanisms against P. tritici-repentis in wheat. This study also implies that saccharides play a role in TS disease development. This illustrates the usefulness of untargeted metabolomics to uncover key information underlying plant-pathogen interactions, as well as to generate testable hypotheses.

Author contributions

LCF, LM, FS, and SS designed the study. LCF conducted and monitored the experiments at NPPC. MB contributed to metabolite data analysis. LCF, LM, FS, and SS interpreted the results. LCF and LM drafted the draft manuscript. All authors participated in the critical reading and writing of the final manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

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Metabolomics links induced responses to the wheat pathogen – tan spot – (Pyrenophora tritici-repentis) to the biosynthesis of flavonoids and bioenergetic metabolism

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Abstract

Introduction

Objectives

Methods

Results

Conclusion

Figures

Introduction

Material and methods

3.1 Host classifications based on lesion scores.

3.2 P. tritici-repentins infections

3.3 Metabolite extractions

3.4 Metabolomic data processing and analysis

Results

4.1 Phenotypic characterisation of Ptr interactions with FH and PF

4.2 Metabolomic characterisation of Ptr interactions with FH and PF

4.3 Insights into responses to Ptr through pathway enrichment and network analysis

Discussion

5.1 Untangling Tan Spot disease metabolism

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1