| Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The C418cultivar is susceptible to infection by Xoo strain PXO99. The transgenic variety, C418-Xa21, and introgression line C418/Xa23through marker assisted selection (MAS) of Xa23gene are resistant.The polar compounds of brown rice were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS) from Lingshui, Hainan island and Changping, Beijing. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to differentiate transgenic and non-transgenci variants. As part of our exploratory phase we performed Hierachical Cluster Analysis (HCA) by grouping the samples into clusters based on the similarity of their metabolite abundance profiles. It was found that both sowing and environment had remarkable impacts on the compositions of transgenic rice and its counterpart.Whether growing in Lingshui, Hainan Island or Changping, Beijing, the metabolic profiles of transgenic and non-transgenic rice were higher similarity compared with C418/Xa23. It possessed distinctive metabolite profile and were more significant differences in metabolic phenotype than transgenic rice compared with the negative control. Furthermore, unique geographical location and climatic conditions had remarkable inpacts on the brown rice metabolism of transgenic and wild rice in Lingshui, Hainan Isaland. It was found that the samples of two different genotypes growing in Hainan Island could not be discriminated on the first principle component and showd cross distribution. Meanwhile, their browen rice samples could be fully distinguished growing in Beijing.Another major finding from the present study was that sowing and environment had no impacts on fructose and tagatose (P<0.05) as the potential biomarkers in brown rice from the two bacterial blight resistance rice varieties. Therefore, when the risk of transgenic rice is assessed in subsequent studies, these steady changed compositions must be taken into account. The unintended compositional changes detected in our study laid a good foundation for further safety assessment of transgenic rice. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation and not limited to recongnized and known metabolites that can be applicable to a wild range of transgenic hybrid rice with more than one foreign gene with no previous optimization.According to metabolic profiles analysis based on GC-MS and quantitative analysis of the resultsof GC-FID, we confirmed that the fatty acid in transgenic rice were vulnerable to the effects of the sowing and environment. It was found that palmitic acid, stearic acid, oleic acid and linoleic acid (P<0.05) only remarkably increased in the Hainan Island, but no significant differences in transgenic rice growing in Beijing. Therefore, these potentional biomarkers may not to be taken into account in futher studies. In addition, the elements (K, P, Na, Zn, and Fe) that varied between transgenic rice and non-transgenic rice growing in Hainnan Island were also analyzed by ANOVA by use of ICP-AES. Except element Fe, other elements were proved to be significantly different. This showed that in the two resistant rice variants C418-Xa21and C418/Xa23, the concentration of element K both decreased at10%, whereas the concentration of elements Na merely decreased17%. The difference of C418-Xa2Iand C418/Xa23was that the concentration of Zn increased24%and12%, respectively, whereas P content decreased11%, although still in the reference range value reported for rice.To rule out the effect of sowing, environment and soil compositon on the rice metabolism, Non-transgenic japonica restorer line C418and C418/Xa23were firstly as the controls under hydroponics in the green house, metabolic profiles of transgenic rice variant C418-Xa21and non-transgenic varieties were compared to assess the unintended effects related to gene modification. The polar compounds of brown rice, root and leaf were extracted, trimethylsilylated and analysis by gas chromatography-mass spectrometry (GC-MS). It was found that the degree of interference was significant differences on different tissues, least affected on brown rice and root tissues, followed by leaves in the largest. Insterestingly, fructose and tagatose (P<0.05) still decreased in brown rice from the two bacterial blight resistance rice varieties and validated them as biomarkers. In addition, C418/Xa23on the metabolism with a distinctive metabolite profile in the brown rice, root and leaf tissues, the transgenic rice C418-Xa21and its counterpart C418only in the rice leaf tissue metabolism have significantly different levels of metabolic phenotypes. However, the leaf tissue metabolic profiles of the transgenic rice and its counterpart showed stronger similarity compared with C418/Xa23. As for brown rice and root tissue, HCA results showed that the samples of transgenic rice and its counterpart were shown to cross distribution and could not be discriminated.In addition, unsupervised and supervised classification tools clearly discriminate C418-Xa21challenged PXO99based LC-TOF/MS analysis. Unbiased, discovery-based metabolomics analyses yielded novel insights into the rice response to Xoo. Our results reveal global metabolic changes in leaf tissues of the XA21transgenic variant challenged with PXO99. While central carboncatabolism is reduced in correlationmetabolite expression in C418-Xa21rice genotype, some alkaloids were increased specifically in the XA21-mediated response to PXO99. The outcome of metabolomics studies such as these will aid in a better understanding of complex response to pathogen infection.In conclusion, our results suggested metabolic profiles analyses can be used to the identification and classification in the metabolic level from innovative rice varieties, and this study provides experimental evidence for application of metabolomics in genetic modified varieties satefy evaluation in the future. Furthermore, the results in the present study confirmed that a single gene change has little effect on biochemical pathway, this was interpreted as a pleiotropic effect of the primary gentic alteration but not found in pathway that were later determined to be involved in resistance to bacterial infection. According to the GC-MS data, supervised multivariate statistics demonstrated the metabolic changes caused throught marker assisted selection techniques were, in these cases, at least of a comparable magnitude to those resulting as an unintended effect of genetic engineering techniques. For the first time using non-transgenic susceptible and resistant controls with the same genetic background to evaluate the unintended effects and metabolic profiles of GM crops will be provided an important reference value in further safety assessment of GM crops. |
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