Rao, J.Yang, L.Guo, J.Quan, S.Chen, G.Zhao, X.Zhang, D.Shi, J.2017-04-232017-04-232016Plant Cell Reports, 2016; 35(2):429-4370721-77141432-203Xhttp://hdl.handle.net/2440/104607Key message: Non-targeted metabolomics analysis revealed only intended metabolic changes in transgenic maize over-expressing the Aspergillus niger phyA2. Abstract: Genetically modified (GM) crops account for a large proportion of modern agriculture worldwide, raising increasingly the public concerns of safety. Generally, according to substantial equivalence principle, if aGMcrop is demonstrated to be equivalently safe to its conventional species, it is supposed to be safe. In this study, taking the advantage of an established non-target metabolomic profiling platform based on the combination of UPLC-MS/MS with GC–MS, we compared the mature seed metabolic changes in transgenicmaize over-expressing the Aspergillus niger phyA2 with its non-transgenic counterpart and other 14 conventional maize lines. In total, levels of nine out of identified 210 metabolites were significantly changed in transgenic maize as compared with its non-transgenic counterpart, and the number of significantly altered metabolites was reduced to only four when the natural variations were taken into consideration. Notably, those four metabolites were all associated with targeted engineering pathway. Our results indicated that although both intended and non-intended metabolic changes occurred in the mature seeds of this GM maize event, only intended metabolic pathway was found to be out of the range of the natural metabolic variation in the metabolome of the transgenic maize. Therefore, only when natural metabolic variation was taken into account, could non-targeted metabolomics provide reliable objective compositional substantial equivalence analysis on GM crops.en© Springer-Verlag Berlin Heidelberg 2015GC–MS; phytase; safety assessment; transgenic; substantial equivalence; UPLC-MS/MSJournal article003004739910.1007/s00299-015-1894-60003712404000122-s2.0-84955350206241910Zhang, D. [0000-0003-3181-9812]