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An integrated RNAseq-1H NMR metabolomics approach to understand soybean primary metabolism regulation in response to Rhizoctonia foliar blight disease

Overview of attention for article published in BMC Plant Biology, April 2017
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Title
An integrated RNAseq-1H NMR metabolomics approach to understand soybean primary metabolism regulation in response to Rhizoctonia foliar blight disease
Published in
BMC Plant Biology, April 2017
DOI 10.1186/s12870-017-1020-8
Pubmed ID
Authors

Tanya R. Copley, Konstantinos A. Aliferis, Daniel J. Kliebenstein, Suha H. Jabaji

Abstract

Rhizoctonia solani AG1-IA is a devastating phytopathogen causing Rhizoctonia foliar blight (RFB) of soybean worldwide with yield losses reaching 60%. Plant defense mechanisms are complex and information from different metabolic pathways is required to thoroughly understand plant defense regulation and function. Combining information from different "omics" levels such as transcriptomics, metabolomics, and proteomics is required to gain insights into plant metabolism and its regulation. As such, we studied fluctuations in soybean metabolism in response to R. solani infection at early and late disease stages using an integrated transcriptomics-metabolomics approach, focusing on the regulation of soybean primary metabolism and oxidative stress tolerance. Transcriptomics (RNAseq) and metabolomics ((1)H NMR) data were analyzed individually and by integration using bidirectional orthogonal projections to latent structures (O2PLS) to reveal possible links between the metabolome and transcriptome during early and late infection stages. O2PLS analysis detected 516 significant transcripts, double that reported in the univariate analysis, and more significant metabolites than detected in partial least squares discriminant analysis. Strong separation of treatments based on integration of the metabolomes and transcriptomes of the analyzed soybean leaves was revealed, similar trends as those seen in analyses done on individual datasets, validating the integration method being applied. Strong fluctuations of soybean primary metabolism occurred in glycolysis, the TCA cycle, photosynthesis and photosynthates in response to R. solani infection. Data were validated using quantitative real-time PCR on a set of specific markers as well as randomly selected genes. Significant increases in transcript and metabolite levels involved in redox reactions and ROS signaling, such as peroxidases, thiamine, tocopherol, proline, L-alanine and GABA were also recorded. Levels of ethanol increased 24 h post-infection in soybean leaves, and alcohol dehydrogenase (ADH) loss-of-function mutants of Arabidopsis thaliana had higher necrosis than wild type plants. As a proof-of-concept, this study offers novel insights into the biological correlations and identification of candidate genes and metabolites that can be used in soybean breeding for resistance to R. solani AG1-IA infection. Additionally, these findings imply that alcohol and its associated gene product ADH may have important roles in plant resistance to R. solani AG1-IA causing foliar blight.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Argentina 1 3%
Unknown 33 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 43%
Researcher 6 17%
Unspecified 3 9%
Student > Master 3 9%
Professor 2 6%
Other 6 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 54%
Unspecified 6 17%
Biochemistry, Genetics and Molecular Biology 6 17%
Chemical Engineering 1 3%
Economics, Econometrics and Finance 1 3%
Other 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 April 2017.
All research outputs
#7,172,623
of 9,746,405 outputs
Outputs from BMC Plant Biology
#793
of 1,337 outputs
Outputs of similar age
#171,164
of 262,048 outputs
Outputs of similar age from BMC Plant Biology
#5
of 5 outputs
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