Metabolomics extraction

Maharjan, R. P. Ferenci,T. Global metabolite analysis the influence of extraction methodology on metabolome profiles of Escherichia coli. Anal. Biochem. 2003,313, 154-154. 

Figure 3.2 Our approach to surmounting the metabolome obstacles of chemical complexity and dynamic range employs sequential extraction followed by parallel analyses. Segregation of the metabolome into subclasses helps minimize chemical interferences, while parallel analyses help to visualize a greater portion of the metabolome.

As in the other -omics, analyses may be directed at a specific metabolite, at all metabolites in a given system in a shot-gun approach, or at accessible groups of molecules in profiling experiments. In that also the technology varies. In addition, the chemistry of different metabolites is very heterogeneous since it involves hydrophobic lipids, hydrophilic carbohydrates, ionic inorganic species, and other secondary natural products and already the choice of solvent in metabolite extraction dictates which types of molecules will be present (Fig. 10.8). Therefore, total metabolome profiling is not possible, because no analytical method will be able to accommodate all the different molecule classes at once. 

All MS technologies require the establishment of method-specific mass libraries so that compounds in the spectra can be identified [212], a tedious task that has been restricted to large laboratories. Nevertheless, some of these efforts are driven by the metabolomics community, thereby requiring some sort of standardization to conduct comparable experiments, as has been proposed with the ArMet standard [216], Last but not the least, metabolomics experiments generate large amounts of data that need sophisticated analysis methods to extract biological information, usually based on multidimensional statistics [3, 5, 58, 209, 217, 218]. Metabolomics experiments as the basis for an analysis of the possible dynamics of metabolic networks are discussed in Section VIII. 

Prior to metabolomic analysis, sample treatment is typically needed, as CSF contains approximately 0.3 mg/mL protein (114) that may hinder metabolite analysis. Consequently, CSF sample treatment is essentially directed to protein removal by means of organic solvent addition (84,88) or by ultrafiltration (85,89,90). The final metabolic extract composition will depend in a great extent on the sample treatment (115), and it will be selected mostly regarding the metabolomic approach and the analytical technique that will be afterward applied. 

Fourier transform ion-cyclotron (FT-ICR-MS) provides the highest mass resolution and accuracy, and enables the determination of the elemental compositions of metabolites, which facilitates annotation procedures for unknown compounds (95). Direct infusion analysis of plant extract without a previous separation and/or derivatization can be achieved however, its use is very restricted due to the equipment cost, the difficulties in hardware handling, and the extremely large amount of data generated. Takahashi et al. applied this technique to elucidate the effects of the overexpression of the YK1 gene in stress-tolerant GM rice (96). More than 850 metabolites could be determined, and the metabolomics fingerprint in callus, leaf, and panicle was significantly different from one another. 

The study of the composition of a mixture is an extremely common problem in analytical and bioanalytical chemistry. While chromatography and solvent extraction are commonly employed to simplify the analysis prior to characterization of the constituents, NMR has provided a series of tools that help in unravelling the components of complex samples, when a previous separation of the pure compounds is not feasible or complete. Thus, TOCSY, NMR diffusometry (DOSY, among all) and heteronuclear correlation experiments are widely used to this purpose, for example, for the characterization of small molecules in biologically relevant samples, such as in metabolomics,1 plant extracts analysis,2 food quality control,3 4 to name a few cases. 

Fig. 11 Data from metabolomics PMI experiments, (a) Enrichment of retinoic acid by CRABP-GST from a mixture of brain lipids ( P < 0.01, n = 3 1, Student s t-test). (b) StarD3-mediated enrichment of cholesterol from a brain lipid extract...

Resin-bound FABP-GST was incubated with the complex lipid mixture. After incubation for 1 h, the mixture was removed, the beads washed, and the protein eluted through addition of glutathione. Analysis of the eluate by global metabolite profiling and XCMS revealed the specific enrichment of fatty acids by FABP from the lipid extracts (Table 2). More specifically, oleic acid, linoleic acid, and arachidonic acid were all enriched and this is consistent with observed lipid specificity of FABP2. This example demonstrates the ability of PMI metabolomics to identify natural binding partners from complex mixtures of natural lipids. 

A solution to this hurdle was first given by genomics, when several genome-wide techniques such as transcriptome and metabolome analysis started to be routinely applied on microbial systems. These techniques, besides requiring significant expertise in data analysis [217], allow the extraction of a vast quantity of information. Unfortunately, the sole presence of this wealth of data is not sufficient to understand the cell behavior from a holistic perspective. To address this issue,... 

In what many consider to be a landmark publication on metabolomics, Fiehn et al. (2000) state it is crucial to perform unbiased (metabolite) analyses in order to define precisely the biochemical function of plant metabolism. The authors argue that for metabolomics/metabolite profiling to become a robust and sensitive method suited to automation, a mature technology such as gas chromatography-mass spectrometry (GC-MS) is required as an analytical technique. The authors go on to describe a simple sample preparation and analysis regime that allowed for the detection and quantification of more than 300 compounds from a single-leaf sample extract. 

The increasing accessibility of bench-top LC-MS systems to researchers of all disciplines, combined with the tandem and high-resolution mass spectrometry capabilities of such instruments, will only increase the number of applications to which LC-MS can be directed. The examples documented in this chapter illustrate some of the diversity and power of the techniques, including analytical applications for known analytes in various matrices, metabolomic analysis, the tentative structural identification of novel compounds, and the screening of extracts for minor, and perhaps novel, components of the alkaloidal profile of plants. 

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