Comparative Metabolomic Profiling of Compatible and Incompatible Interactions Between Potato and Phytophthora infestans

Abstract

Late blight is one of the main biological stresses limiting the potato yield; however, the biochemical mechanisms underlying the infection process of Phytophthora infestans remain unrevealed. In this study, the late blight-resistant potato cultivar Ziyun No.1 (R) and the susceptible cultivar Favorita (S) were inoculated with P. infestans. Untargeted metabolomics was used to study the changes of metabolites in the compatible and incompatible interactions of the two cultivars and the pathogen at 0, 48, and 96 h postinoculation (hpi). A total of 819 metabolites were identified, and the metabolic differences mainly emerged after 48 hpi. There were 198 and 115 differentially expressed metabolites (DEMs) in the compatible and incompatible interactions. These included 147 and 100 upregulated metabolites during the compatible and incompatible interactions, respectively. Among them, 73 metabolites were identified as the P. infestans-responsive DEMs. Furthermore, the comparisons between the two cultivars identified 57 resistance-related metabolites. Resistant potato cultivar had higher levels of salicylic acid and several upstream phenylpropanoid biosynthesis metabolites, triterpenoids, and hydroxycinnamic acids and their derivatives, such as sakuranetin, ferulic acid, ganoderic acid Mi, lucidenic acid D2, and caffeoylmalic acid. These metabolites play crucial roles in cell wall thickening and have antibacterial and antifungal activities. This study reports the time-course metabolomic responses of potatoes to P. infestans. The findings reveal the responses involved in the compatible and incompatible interactions of potatoes and P. infestans.

Keywords: Phytophthora infestans; compatible; incompatible; metabolomics; potato cultivars.

FIGURE 1

Phenotypic changes of potato leaves at different time points of Phytophthora infestans infection. Virus-free seedlings of Favorita (A) and Ziyun No.1 (B) inoculated with P. infestans isolate SCPZ16-3-1 at 0 h before infection (hbi), 48 h postinoculation (hpi), and 96 hpi.

FIGURE 2

Correlation analyses of the different samples collected from the compatible and incompatible interactions. (A) Sample correlation Partial Least Squares-Discriminant Analysis (PLS-DA) diagram. (B) Sample correlation heatmap. R1, R2, and R3 represent the samples collected at 0, 48, and 96 hpi, respectively, in incompatible interactions. S1, S2, and S3 denote the samples collected at 0, 48, and 96, respectively, in the compatible interactions. QC, quality control sample.

FIGURE 3

Differentially expressed metabolites (DEMs) identified during the infection process. (A) DEMs identified during the compatible interactions. (B) DEMs identified during the incompatible interactions. (C) DEMs identified between the compatible and incompatible interactions. (D) Change trend of the DEMs in the compatible (the values before the slash) and the incompatible (the values after the slash) interactions.

FIGURE 4

Expression levels of some annotated metabolites.

FIGURE 5

Metabolites expression patterns between the compatible and incompatible interaction at 96 hpi. (A) Metabolites with altered abundances [irrespective of the p-value and variable importance in projection (VIP) value] at 96 hpi were divided into ten subclusters according to the expression patterns; each column represents a sample, each row represents a metabolite, and the color indicates the relative abundance of metabolites. (B) Number of metabolites for each subcluster.

FIGURE 6

Expression patterns of (A) phenylpropanoids biosynthesis- and (B) terpenoids-related metabolites. Each row represents a metabolite, while each column represents metabolite comparisons. For example, the S3vsS1 column means the log₂ (S3/S1). The heatmap was developed via HemI toolkit using log₂ fold change (log₂FC) values (Deng et al., 2014).