Metabolomics is a powerful method for comprehensive investigation of metabolite variations in biological systems. Currently liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) represent the most powerful metabolomic platform. Provided that a proper sample preparation is performed, this technology may allow for the detection of thousands of metabolites and therefore may provide a comprehensive view of the metabolome. Untargeted metabolite mass profiles can be used for biological interpretations, however approaches that do not require the identification of the metabolic features should be used with extreme caution because they may lead to false interpretations. The identification of metabolites with a high level of confidence is required in order to improve the meaning of metabolomics in biological systems, such as plant-pathogen interaction, and possible applications. Well-established computational tools and workflows are important for highly reproducible and repeatable metabolomic studies. In addition, knowledge-based workflows for metabolite annotations should be built, integrating information relevant to MS peaks relationships (adducts and neutral losses), MS/MS data, retention time modeling, with biochemical knowledge. Sharing workflows (research data and software) helps to validate the findings reported in publications and, more importantly, lets researchers freely reuse the data as they are or as a reliable basis to move forward. For these reasons, the aim of this study was to develop an integrated and open source platform for LC-HRMS metabolomic studies. The platform enables processing of data from targeted and untargeted LC-HRMS analysis (profiling and compound annotation). The applicability of the developed approach is here demonstrated through a preliminary investigation of the metabolic response of maize induced by Fusarium verticillioides infection in maize kernels.
Development of an integrated and open source platform for LC-HRMS metabolomic studies. Case study: metabolic response of maize induced by Fusarium verticillioides infection.
Ciasca B;Pascale M;Logrieco AF;
2018
Abstract
Metabolomics is a powerful method for comprehensive investigation of metabolite variations in biological systems. Currently liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) represent the most powerful metabolomic platform. Provided that a proper sample preparation is performed, this technology may allow for the detection of thousands of metabolites and therefore may provide a comprehensive view of the metabolome. Untargeted metabolite mass profiles can be used for biological interpretations, however approaches that do not require the identification of the metabolic features should be used with extreme caution because they may lead to false interpretations. The identification of metabolites with a high level of confidence is required in order to improve the meaning of metabolomics in biological systems, such as plant-pathogen interaction, and possible applications. Well-established computational tools and workflows are important for highly reproducible and repeatable metabolomic studies. In addition, knowledge-based workflows for metabolite annotations should be built, integrating information relevant to MS peaks relationships (adducts and neutral losses), MS/MS data, retention time modeling, with biochemical knowledge. Sharing workflows (research data and software) helps to validate the findings reported in publications and, more importantly, lets researchers freely reuse the data as they are or as a reliable basis to move forward. For these reasons, the aim of this study was to develop an integrated and open source platform for LC-HRMS metabolomic studies. The platform enables processing of data from targeted and untargeted LC-HRMS analysis (profiling and compound annotation). The applicability of the developed approach is here demonstrated through a preliminary investigation of the metabolic response of maize induced by Fusarium verticillioides infection in maize kernels.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
