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. 2021 Mar 19;11(3):179.
doi: 10.3390/metabo11030179.

Untargeted Metabolomics Analysis by UHPLC-MS/MS of Soybean Plant in a Compatible Response to Phakopsora pachyrhizi Infection

Evandro Silva  1 , José Perez da Graça  2 , Carla Porto  1   3 , Rodolpho Martin do Prado  1 , Estela Nunes  4 , Francismar Corrêa Marcelino-Guimarães  2 , Mauricio Conrado Meyer  2 , Eduardo Jorge Pilau  1
Affiliations

Untargeted Metabolomics Analysis by UHPLC-MS/MS of Soybean Plant in a Compatible Response to Phakopsora pachyrhizi Infection

Evandro Silva et al. Metabolites. .

Abstract

Phakopsora pachyrhizi is a biotrophic fungus, causer of the disease Asian Soybean Rust, a severe crop disease of soybean and one that demands greater investment from producers. Thus, research efforts to control this disease are still needed. We investigated the expression of metabolites in soybean plants presenting a resistant genotype inoculated with P. pachyrhizi through the untargeted metabolomics approach. The analysis was performed in control and inoculated plants with P. pachyrhizi using UHPLC-MS/MS. Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), was applied to the data analysis. PCA and PLS-DA resulted in a clear separation and classification of groups between control and inoculated plants. The metabolites were putative classified and identified using the Global Natural Products Social Molecular Networking platform in flavonoids, isoflavonoids, lipids, fatty acyls, terpenes, and carboxylic acids. Flavonoids and isoflavonoids were up-regulation, while terpenes were down-regulated in response to the soybean-P. pachyrhizi interaction. Our data provide insights into the potential role of some metabolites as flavonoids and isoflavonoids in the plant resistance to ASR. This information could result in the development of resistant genotypes of soybean to P. pachyrhizi, and effective and specific products against the pathogen.

Keywords: Asian soybean rust; GNPS; Phakopsora pachyrhizi; UHPLC-MS/MS; chemometrics; metabolomics; soybean.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Unsupervised chemometric modeling (UHPLC-ESI-(+)-MS/MS data): (A) PCA scores of 18 samples (triplicate of 6 control plants and 6 inoculated plants), green circles correspond to control plant samples (C), red triangle corresponds to inoculated plant samples (I). (B) The scores plot in (A) but colored according to triplicate each plant (P). 5 PCs explained 51.6% of the total data variance.
Figure 2
Figure 2
Partial least-squares discriminant analysis (PLS-DA) of 18 samples (triplicate of 6 control plants and 6 inoculated plants), green circles correspond to control plant samples (C), red circles correspond to inoculated plant samples (I). The classification model was built to screen potential biomarkers using VIP values.
Figure 3
Figure 3
Molecular network of the MS/MS spectra obtained by the analysis of the soybean control plants, or inoculated with P. pachyrhizi colored by 6 chemical class terms selected as indicated in the legend annotated on the molecular network (Supplementary Figure S5) using the MolNetEnhancer. The black bold borders nodes represent the MS/MS spectra that had hits with the spectra of the GNPS libraries.
Figure 4
Figure 4
Cluster of terpenes containing triterpene saponins putatively characterized by molecular network obtained from MS/MS data from control plants and inoculated plants with P. pachyrhizi. The edge width represents the cosine score (0.88 to 0.99). The edge label represents the mass difference between nodes (2.016 Da, 15.995 Da, 18.011 Da, 132.041 Da, 146.058 Da, and 162.052 Da). The black bold borders nodes represent the MS/MS that had hits with the spectra of the GNPS libraries. The pie chart within each node corresponds to the percentage relative of the metabolite in the sample, green indicates soybean control plants, and red when soybean plants were inoculated with P. pachyrhizi.
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