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Targeted metabolomics analysis of fatty acids in soybean seeds using GC-MS to reveal the metabolic manipulation of shading in the intercropping system

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Abstract

A field microenvironment varies with intercropping ecological planting patterns, especially its light conditions. Due to light reflection and absorption by maize leaves, spectral irradiance, red/far-red (R/FR) ratio, and photosynthetic active radiation (PAR) of the soybean canopy are decreased in a maize–soybean intercropping system as compared to those in sole cropping. In this study, a metabolomics method was applied to analyze fatty acid metabolism of soybeans grown under an intercrop shading condition. The results indicate that the fatty acid contents of the intercropped soybean were significantly higher than those of the sole-cropped soybeans. As the shading effect increased, the soybean fatty acids content showed a rise and fall tendency. Moreover, the partial least-squares discriminant analysis (PLS-DA), Pearson correlation, and hierarchical clustering analysis (HCA) multivariate analyses showed a similar result. The results suggested that shading can manipulate soybean fatty acid metabolism in a maize–soybean strip intercropping system. Further, the metabolite profiling combined with multivariate statistical analysis can be used as a useful tool for identifying the metabolic links between fatty acid metabolites.

Graphical abstract: Targeted metabolomics analysis of fatty acids in soybean seeds using GC-MS to reveal the metabolic manipulation of shading in the intercropping system

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Publication details

The article was received on 02 Jan 2017, accepted on 03 Mar 2017, published on 06 Mar 2017 and first published online on 06 Mar 2017


Article type: Paper
DOI: 10.1039/C7AY00011A
Citation: Anal. Methods, 2017, Advance Article
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    Targeted metabolomics analysis of fatty acids in soybean seeds using GC-MS to reveal the metabolic manipulation of shading in the intercropping system

    C. Yang, N. Iqbal, B. Hu, Q. Zhang, H. Wu, X. Liu, J. Zhang, W. Liu, W. Yang and J. Liu, Anal. Methods, 2017, Advance Article , DOI: 10.1039/C7AY00011A

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