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Polycyclic aromatic hydrocarbons are an important group of persistent organic pollutants. Using plants to remediate PAHs has been recognized as a cost-effective and environmentally friendly technique. However, the overall impact of PAHs on the regulation of plant metabolism has not yet been explored. In this study, we analyzed the alteration in the maize (Zea mays L.) metabolome on exposure to high molecular weight PAHs such as benzo[a]pyrene (BaP) and pyrene (PYR) in a hydroponic medium, individually and as a mixture (BaP + PYR) using GC-MS. The differences in the metabolites were analyzed using XCMS (an acronym for various forms (X) of chromatography-mass spectrometry), an online-based data analysis tool. A significant variation in metabolites was observed between treatment groups and the unspiked control group. The univariate, multivariate and pathway impact analysis showed there were more significant alterations in metabolic profiles between individual PAHs and the mixture of BaP and PYR. The marked changes in the metabolites of galactose metabolism and aminoacyl tRNA biosynthesis in PAHs treated maize leaves exhibit the adaptive defensive mechanisms for individual and PAHs mixture. Therefore, the metabolomics approach is essential for an understanding of the complex biochemical responses of plants to PAHs contaminants. This knowledge will shed new light in the field of phytoremediation, bio-monitoring, and environmental risk assessment.
Pubmed ID: 30296765
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A public repository of metabolite information as well as tandem mass spectrometry data is provided to facilitate metabolomics experiments. It contains structures and represents a data management system designed to assist in a broad array of metabolite research and metabolite identification. An annotated list of known metabolites and their mass, chemical formula, and structure are available. Each metabolite is linked to outside resources for further reference and inquiry. MS/MS data is also available on many of the metabolites.
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View all literature mentionsWeb server for statistical, functional and integrative analysis of metabolomics data. Web based tool suite used for metabolomic data processing, normalization, multivariate statistical analysis, and data annotation, biomarker discovery and classification.
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