Abstract
BackgroundMetabolic phenotyping has become an important ‘bird's-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of ‘top-down’ chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems.Methodology/Principal FindingsThe analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 1H and 13C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each 13C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient 13C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts.Conclusions/SignificanceOverall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of 13C atoms of given metabolites on development-dependent changes in the 56 identified 13C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism.
Highlights
The tremendous advances in DNA sequencing technologies that have taken place over the last few years have made large-scale sequencing and genome comparison studies accessible for many applications
The database contains only highly reproducible chemical shifts, which makes it a reliable source for identifying metabolic products. It consists of 1018 1H and 13C chemical shifts corresponding to 142 metabolites
Each chemical shift was derived from an nuclear magnetic resonance (NMR) spectrum for one or a few metabolites measured at 298 K and dissolved in 100 mM potassium phosphate buffer at pH 7.0
Summary
The tremendous advances in DNA sequencing technologies that have taken place over the last few years have made large-scale sequencing and genome comparison studies accessible for many applications. The cost of human genotype characterization is about $1000 per genome [1]. These technologies have made comparative metagenomic analyses of complex microbial communities sampled from diverse environments possible [2]. Versatile sampling and robust 1H-NMR methods have improved disease diagnosis [9,10,11,12] and personalized healthcare [13,14,15], and have provided an important tool for the characterization of genetic variations [16]. Metabolic phenotyping has become an important ‘bird’s-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. We describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems
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