Targeted metabolomics analysis of fatty acids in soybean seeds using GC-MS to reveal the metabolic manipulation of shading in the intercropping systemElectronic supplementary information (ESI) available. See DOI: 10.1039/c7ay00011a

• Main Authors: Yang, Cai-qiong, Iqbal, Nasir, Hu, Bao-yu, Zhang, Qi-hui, Wu, Hai-jun, Liu, Xin, Zhang, Jing, Liu, Wei-guo, Yang, Wen-yu, Liu, Jiang
• Format: Journal Article
• Language: English
• Published: 06.04.2017
• ISSN: 1759-9660; 1759-9679
• DOI: 10.1039/c7ay00011a

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. Targeted metabolomics analysis was conducted to quantify fatty acids in soybean seeds using GC-MS. PLS-DA helped to determine the appropriate row spacing in the intercropping system.