Abstract
Metabolite extraction is one of the critical steps in microbial metabolome analysis. It affects both the observed metabolite content and biological interpretation of the data. Several methods exist for metabolite extraction of microbes, but the literature is not consistent regarding the sample model, adequacy, and performance of each method. In this study, an optimal extraction protocol for Yersinia intracellular metabolites was investigated. The effect of five extraction protocols consisting of different extraction solvent systems (60% methanol, 100% methanol, acetonitrile/methanol/water (2:2:1), chloroform/methanol/water (2:1:1), and 60% ethanol) on Yersinia metabolic profiles were compared. The number of detected peaks, sample-to-sample variation, and metabolite yield were used as criteria. Extracted metabolites were analyzed by 1H-NMR and principal component analysis (PCA), as well as partial least squares discriminant analysis (PLS-DA) multivariate statistics. The extraction protocol using 100% methanol as the extraction solvent provided the highest number of detected peaks for both Yersinia species analyzed, yielding more spectral information. Together with the reproducibility and spectrum quality, 100% methanol extraction was suitable for intracellular metabolite extraction from both species. However, depending on the metabolites of interest, other solvents might be more suitable for future studies, as distinct profiles were observed amongst the extraction methods.
Highlights
The metabolome of any organism is complex, containing thousands of chemically and physically diverse metabolites [1,2]
Comparison of representative spectra for Y. enterocolitica (Figure 1) and Y. pseudotuberculosis (Figure 2) showed that the quality of spectra between sample classes are similar in terms of resolution
Principal component analysis was used to compare the sample-to-sample variation of metabolic fingerprints derived from six replicates per method for both Y. enterocolitica and Y. pseudotuberculosis
Summary
The metabolome of any organism is complex, containing thousands of chemically and physically diverse metabolites [1,2]. The goal of untargeted metabolomics is to obtain as much information about the metabolome as possible using efficient, reproducible, and relatively affordable methods. Comparison and optimization of methods at the sample preparation, analysis, and interpretation levels are crucial [3]. Metabolomics methods should be optimized for each type of sample organism of interest [4,5,6]. The spectrum of a 1D 1H-NMR experiment contains a complex mixture of proton signals plotted along a chemical shift value (ppm) which describes the chemical environment and structural characteristics of specific metabolites. The intensity values of each proton signal are directly proportional to the concentration of the corresponding metabolite. 1D 1H-NMR coupled with multivariate statistics in metabolomics is commonly employed for the global analysis of the metabolome [8,9]
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