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Metabolic response induced by parasitic plant-fungus interactions hinder amino sugar and nucleotide sugar metabolism in the host.

ABSTRACT: Infestation by the biotrophic pathogen Gymnosporangium asiaticum can be devastating for plant of the family Rosaceae. However, the phytopathology of this process has not been thoroughly elucidated. Using a metabolomics approach, we discovered the intrinsic activities that induce disease symptoms after fungal invasion in terms of microbe-induced metabolic responses. Through metabolic pathway enrichment and mapping, we found that the host altered its metabolite levels, resulting in accumulation of tetrose and pentose sugar alcohols, in response to this fungus. We then used a multiple linear regression model to evaluate the effect of the interaction between this abnormal accumulation of sugar alcohol and the group variable (control/parasitism). The results revealed that this accumulation resulted in deficiency in the supply of specific sugars, which led to a lack of amino sugar and nucleotide sugar metabolism. Halting this metabolism could hamper pivotal functions in the plant host, including cell wall synthesis and lesion repair. In conclusion, our findings indicate that altered metabolic responses that occur during fungal parasitism can cause deficiency in substrates in pivotal pathways and thereby trigger pathological symptoms.

SUBMITTER: Lee DK

PROVIDER: S-EPMC5124995 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Metabolic response induced by parasitic plant-fungus interactions hinder amino sugar and nucleotide sugar metabolism in the host.

Lee Dong-Kyu DK Ahn Soohyun S Cho Hae Yoon HY Yun Hye Young HY Park Jeong Hill JH Lim Johan J Lee Jeongmi J Kwon Sung Won SW

Scientific reports 20161128

Infestation by the biotrophic pathogen Gymnosporangium asiaticum can be devastating for plant of the family Rosaceae. However, the phytopathology of this process has not been thoroughly elucidated. Using a metabolomics approach, we discovered the intrinsic activities that induce disease symptoms after fungal invasion in terms of microbe-induced metabolic responses. Through metabolic pathway enrichment and mapping, we found that the host altered its metabolite levels, resulting in accumulation of ...[more]

PMID: 27892480

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Project description:Nucleotide-diphospho-sugars (NDP-sugars) are the building blocks of diverse polysaccharides and glycoconjugates in all organisms. In plants, 11 families of NDP-sugar interconversion enzymes (NSEs) have been identified, each of which interconverts one NDP-sugar to another. While the functions of these enzyme families have been characterized in various plants, very little is known about their evolution and origin. Our phylogenetic analyses indicate that all the 11 plant NSE families are distantly related and most of them originated from different progenitor genes, which have already diverged in ancient prokaryotes. For instance, all NSE families are found in the lower land plant mosses and most of them are also found in aquatic algae, implicating that they have already evolved to be capable of synthesizing all the 11 different NDP-sugars. Particularly interesting is that the evolution of RHM (UDP-L-rhamnose synthase) manifests the fusion of genes of three enzymatic activities in early eukaryotes in a rather intriguing manner. The plant NRS/ER (nucleotide-rhamnose synthase/epimerase-reductase), on the other hand, evolved much later from the ancient plant RHMs through losing the N-terminal domain. Based on these findings, an evolutionary model is proposed to explain the origin and evolution of different NSE families. For instance, the UGlcAE (UDP-D-glucuronic acid 4-epimerase) family is suggested to have evolved from some chlamydial bacteria. Our data also show considerably higher sequence diversity among NSE-like genes in modern prokaryotes, consistent with the higher sugar diversity found in prokaryotes. All the NSE families are widely found in plants and algae containing carbohydrate-rich cell walls, while sporadically found in animals, fungi and other eukaryotes, which do not have or have cell walls with distinct compositions. Results of this study were shown to be highly useful for identifying unknown genes for further experimental characterization to determine their functions in the synthesis of diverse glycosylated molecules.

Project description:Disease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods. Specifically, the objective of this research was to identify drivers of wilt in commercial mint fields, describe the relationships between these drivers, and predict wilt. Soil samples were collected from commercial mint fields. Wilt foci, V. dahliae, and plant-parasitic nematodes that can exacerbate wilt were quantified. Multiple linear regression, a generalized additive model, random forest, and an artificial neural network were fit to the data, validated with 10-fold cross-validation, and measures of explanatory and predictive performance were compared. All models selected nematodes within the genus Pratylenchus as the most important predictor of wilt. The fungus after which this disease is named, V. dahliae, was the fourth most important predictor of wilt, after crop age and cultivar. All models explained around 50% of the total variation (R2 ≤ 0.46), and exhibited comparable predictive error (RMSE ≤ 1.21). Collectively, these models revealed that the quantitative relationships between two pathogens, mint cultivars and age are required to explain wilt. The ascendance of Pratylenchus spp. in predicting symptoms of a disease assumed to primarily be caused by V. dahliae exposes the underestimated contribution of these nematodes to wilt. This research provides a foundation on which predictive forecasting tools can be developed for mint growers and reminds us of the lessons that can be learned by revisiting assumptions about disease etiology.

Dataset Information

Metabolic response induced by parasitic plant-fungus interactions hinder amino sugar and nucleotide sugar metabolism in the host.

Publications

Metabolic response induced by parasitic plant-fungus interactions hinder amino sugar and nucleotide sugar metabolism in the host.

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