RESUMO
We evaluated Isotope Ratio Outlier Analysis (IROA) as a metabolome-wide internal standard approach to improve the quality of LC/MS data collected from a large-scale greenhouse experiment designed to metric the ability of metabolomics to model quantitatively nitrogen treatments. We further looked at how IROA would be incorporated into a metabolomics workflow. For this we compared IROA processed data with that generated without the benefit of metabolome-wide internal standards using our current tool, Genedata Expressionist, from the same raw LC/MS data files. In our experiment, 367 maize plants were grown from kernel in a greenhouse under controlled conditions. Plants were treated from germination on with varying concentrations of nutrient nitrogen as one (treatment) variable. A second variable was the presence of one of two transgenes. Metabolomics analysis of leaves was performed by LC/MS positive and negative electrospray ionization modes, and raw data were processed with both our routine and IROA protocols. IROA data analysis detected 184 metabolites in each ionization mode. Analysis without IROA yielded 281 metabolites in positive ionization mode and 172 in negative ionization mode. Data from both protocols were normalized for sample dry weight, location in the greenhouse, extraction batch, sample run order, and internal standard. Normalized results were subjected to partial least squares (PLS) analysis to model the relationship between the metabolome and nitrogen treatment. Without IROA, regression coefficients of 0.819 and 0.849 for positive and negative modes, respectively were achieved. The IROA protocol improved on the values, yielding regression coefficients of 0.876 and 0.879 for positive and negative modes, respectively. In addition, IROA corrected for detector saturation for several high abundant peaks. Our experiment demonstrates that incorporating IROA into an LC/MS metabolomics experiment improves data quality and facilitates more precise modeling of a biological response.
Assuntos
Marcação por Isótopo , Metabolômica , Nitrogênio/metabolismo , Zea mays/metabolismo , Cromatografia Líquida , Espectrometria de Massas , Conformação MolecularRESUMO
We evaluated the variability of metabolites in various maize hybrids due to the effect of environment, genotype, phenotype as well as the interaction of the first two factors. We analyzed 480 forage and the same number of grain samples from 21 genetically diverse non-GM Pioneer brand maize hybrids, including some with drought tolerance and viral resistance phenotypes, grown at eight North American locations. As complementary platforms, both GC/MS and LC/MS were utilized to detect a wide diversity of metabolites. GC/MS revealed 166 and 137 metabolites in forage and grain samples, respectively, while LC/MS captured 1341 and 635 metabolites in forage and grain samples, respectively. Univariate and multivariate analyses were utilized to investigate the response of the maize metabolome to the environment, genotype, phenotype, and their interaction. Based on combined percentages from GC/MS and LC/MS datasets, the environment affected 36% to 84% of forage metabolites, while less than 7% were affected by genotype. The environment affected 12% to 90% of grain metabolites, whereas less than 27% were affected by genotype. Less than 10% and 11% of the metabolites were affected by phenotype in forage and grain, respectively. Unsupervised PCA and HCA analyses revealed similar trends, i.e., environmental effect was much stronger than genotype or phenotype effects. On the basis of comparisons of disease tolerant and disease susceptible hybrids, neither forage nor grain samples originating from different locations showed obvious phenotype effects. Our findings demonstrate that the combination of GC/MS and LC/MS based metabolite profiling followed by broad statistical analysis is an effective approach to identify the relative impact of environmental, genetic and phenotypic effects on the forage and grain composition of maize hybrids.
Assuntos
Cromatografia Líquida/métodos , Cromatografia Gasosa-Espectrometria de Massas/métodos , Metabolômica/métodos , Zea mays/química , Zea mays/genética , Meio Ambiente , Genótipo , Hibridização Genética , Fenótipo , Zea mays/classificação , Zea mays/metabolismoRESUMO
We recently applied gas chromatography coupled to time-of-flight mass spectrometry (GC/TOF-MS) and multivariate statistical analysis to measure biological variation of many metabolites due to environment and genotype in forage and grain samples collected from 50 genetically diverse nongenetically modified (non-GM) DuPont Pioneer commercial maize hybrids grown at six North American locations. In the present study, the metabolome coverage was extended using a core subset of these grain and forage samples employing ultra high pressure liquid chromatography (uHPLC) mass spectrometry (LC/MS). A total of 286 and 857 metabolites were detected in grain and forage samples, respectively, using LC/MS. Multivariate statistical analysis was utilized to compare and correlate the metabolite profiles. Environment had a greater effect on the metabolome than genetic background. The results of this study support and extend previously published insights into the environmental and genetic associated perturbations to the metabolome that are not associated with transgenic modification.