Fig 1.
Disease severity rating and of phenotypical symptom development of the Dunnart oat cultivar after inoculation with Ps-c, Ps-t, DC3000 and hrcC mutant.
(A) Disease severity range from 0 = Symptomless (no visual symptoms are observed), 1 = Limited amount of lesions or yellowing are present (5% of leaf material start presenting visual symptoms), 2 = Lesions and yellowing spread over the surface of the leaves (6%-11% of leaf material present visual symptoms), 3 = Lesions are present in moderate amounts (11%-20% of leaf material present visual symptoms), 4 = Lesions are present in severe quantities (21%-50% of leaf material present visual symptoms), 5 = Limited leaf wilting occurs (6%-11% of leaf material are yellow and wilted). Error bars indicate the standard deviation. (B) At 4 d.p.i. Dunnart showed typical halo blight symptoms upon infection with Ps-c such as the water-soaked lesion and the development of a characteristic yellow halo, small brown necrotic spots were apparent upon infection with Ps-t and typical chlorosis of the leaf upon infection with DC3000, however no visible symptoms were noted for the hrcC− treatment.
Fig 2.
Chemiluminescence determination of reactive oxygen species generation by leaf disks from oat seedlings in response to various P. syringae treatments.
The pathogen interaction and responses are visualised using the luminescence assay that monitors the kinetics of ROS production over time. (A) The rate of ROS production over a period of 0–28 min in response to the respective bacterial treatments. The light released owing due to the oxidation of luminol is represented as relative light units (RLU). The average values ± standard error of the mean (SEM) for each time point on the curve are shown (n = 36). (B) The sum of the integrated area under the curve shows the overall ROS production for the Dunnart cv as relative luminescence units (RLU) over 28 min as a result of the respective treatments (Ps-c, Ps-t, DC3000 and hrcC mutant). A paired Student’s t-test was used to compare the treatments to the control (**** = p < 0.0001).
Fig 3.
Peroxidase (POX) response of oat seedlings to treatment with Ps-c, Ps-t, DC3000 and hrcC mutant.
The assay assesses the activity of POX enzymes generated in response to innate immune activation. Leaf disks from 3-week-old plants (Dunnart cv) were treated with water (control = no bacteria) or with a noted dose (OD600 ≈0.3) of the respective Pseudomonas bacteria. Total POX activity was measured 20 h after treatment and data is shown as the average of the measured values. Graphs represent data averaged from eleven repeated experiments with 3 biological replicates for each sample (n = 33). Error bars represent standard error of the mean, four asterisk (****) indicates p < 0.0001, Student t-tests. The lower quartile value, median value, and upper quartile value are shown in boxes, while the whiskers extend to the lowest and highest values.
Fig 4.
Representative UHPLC-MS base peak intensity (BPI) chromatograms in ESI(−) mode showing the unique metabolite profiles present in leaf extracts of oat seedlings at 2 d.p.i. following treatment with the respective strains of P. syringae (Ps-c, Ps-t, DC3000 and hrcC−).
The BPI chromatograms illustrate evidently differential peak populations (based on presence and intensities) of P. syringae infected vs the untreated control. The linked y-axes indicate the relative peak abundance (%) of the metabolite signatures at their respective retention times (min). The chromatograms are staggered along the x-axis. A representative of each metabolite class is presented below the chromatograms, indicating the elution range.
Fig 5.
PCA score plots of the UHPLC-MS ESI(−) data of leaf extracts from oat seedlings treated with the respective P. syringae pathovars and the corresponding HCA dendrograms.
Two dimensional PCA scores plots showing differential groupings of the treatments (Ps-c, Ps-t, DC3000 and hrcC−) and controls at (A) 2 d.p.i. (B) 4 d.p.i. and (C) 6 d.p.i.. VC (vehicle control, light purple) represents plants sprayed with a solution free of bacteria and HC (healthy control, dark purple) the untreated group. The Hotelling’s T2 is illustrated by an ellipse on the score plot indicating a 95% confidence interval. Ward-linkage HCA dendrograms that correlate to PCA plots A, B and C respectively and demonstrates the hierarchical breakdown of the data both before (control) and after treatment at (D) 2 d.p.i. (E) 4 d.p.i. (F) 6 d.p.i.
Fig 6.
An orthogonal projection to latent structures discriminant analysis (OPLS-DA) model of the control and hrcC mutant infected plants.
(A) An OPLS-DA scores plot summarising the relationship among different datasets to visualise group clustering between the control and infected groups at 4 d.p.i. based on their leaf-extracted metabolic profiles obtained in ESI(–) MS mode (R2 = 0.999, Q2 = 0.995, CV-ANOVA p-value = 1.89349 x 10−15). (B) The corresponding OPLS-DA loadings S-plot of (A). The pink and orange circles indicate the values situated far out (p[1] > 0.05, < -0.05 and p(corr) >0.3, < -0.3) in the S-plot, representing statistically significant ions that are possible discriminatory variables between the control and infected groups. (C) The response permutation test plot (n = 100) for the OPLS-DA model. (D) A receiver operating characteristic (ROC) curve summarises the ability of a binary classifier (OPLS-DA), with a classifier having perfect discrimination producing a ROC curve that passes through the top left corner to indicate 100% sensitivity and specificity.
Table 1.
List of annotated, putatively identified secondary metabolites produced in response to host and nonhost interactions of P. syringae pathovars on oat leaf tissues.
Metabolites were identified according to MSI level 2 guidelines where the accurate mass, fragmentation data, database entries and published literature were used for annotation. These discriminatory metabolites were derived from OPLS-DA S-plots with rigorous statistical validation. VIP scores for all the reported metabolites were > 1.0. Presence or absence as discriminatory metabolites are shown for each treatment.
Fig 7.
Heatmap trends of the relative concentrations of the annotated discriminatory metabolites (Table 1).
The heatmap illustrates the relative intensities using colour intensity to depict the ions detected in each sample. The respective infected and control groups are indicated on the map, which was created using the Pearson distance and Ward’s linkage rule. The mean peak intensities of each detected metabolite are shown following Pareto scaling of the data. Brown is used to indicate values that are higher than average, and blue is used to indicate values that are lower than average. Each row represents discriminant metabolites, and the columns represent treatment groups.
Fig 8.
Venn diagrams illustrating the partial overlap and differences of the identified metabolites among the respective treatments of the oat seedlings by the P. syringae pathovars.
The infected and control groups are compared. The numerical values indicate metabolites (Table 1) that are unique to, and also shared among the treatments at the respective time points. (A) 2 d.p.i. (B) 4 d.p.i. (C) 6 d.p.i.
Fig 9.
Pathway analysis summary of all MetaboAnalyst-computed metabolic pathways displayed according to their significance or pathway impact.
The diagram depicts all the matching pathways, organized by p-values (y-axis; pathway enrichment analysis) and pathway impact values (x-axis; pathway topology analysis). The impact values define the node sizes, and each node is coloured according to its matching p-value. The graph thus indicates the general secondary metabolic biosynthetic pathways as having the greatest impact with the phenylpropanoid pathway as having the greatest significance.
Fig 10.
Pathways flagged from metabolome analysis of leaf extracts of oat seedlings treated with pathovars of P. syringae.
Signatory metabolites involved in each pathway are illustrated in red boxes. (A) The phenylpropanoid pathway, (B) the secondary metabolite pathway overlapping with the phenylpropanoid pathway, and (C) phenylalanine metabolism. (D) The linoleic acid pathway that showed a high impact after pathway enrichment analysis, along with the secondary metabolite biosynthesis pathway. (E) The isoquinoline alkaloid biosynthesis pathway. (F) Phenylalanine, tyrosine and tryptophan metabolism. All annotated metabolites (Table 1) could not be mapped due to limitations in the MetaboAnalyst software. Some extensions have been manually added to the pathways (using KEGG as a guideline) to represent more of the annotated metabolites and the pathways they are involved in. Each of the coloured circles corresponds to the respective treatments (Ps-c, Ps-t, DC3000 and hrcC mutant) or control, illustrating where these metabolites presented as signatory.
Fig 11.
The distribution of the identified metabolite classes across the respective treatments.
This figure summarises differences caused by changes in the underlying metabolic profiles of the oat seedlings treated with (A) Ps-c (host response), (B) Ps-t (type II nonhost response) (C) hrcC mutant and (D) DC3000 (type I nonhost response) respectively. Each pie chart illustrates where a greater number of discriminatory metabolites were identified for each class.