Abstract
A new liquid chromatography mass spectrometry (LC–MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 and 42 °C) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate curve resolution alternating least squares (MCR-ALS) was applied to full spectral scan LC–MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by partial least squares discriminant analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.
Highlights
Cell metabolites describe the physical and chemical characteristics of organisms
A new liquid chromatography mass spectrometry (LC–MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples
A range of analytical platforms are used for metabolomic analysis, including direct infusion mass spectrometry (MS) (Højer-Pedersen et al 2008), gas chromatography coupled to mass spectrometry (GC–MS) (Lu et al 2008), two-dimensional GC coupled to MS (GC 9 GC–MS), liquid chromatography coupled to MS (LC–MS) (Bajad et al 2006), capillary electrophoresis coupled to MS (CE–MS), and proton nuclear magnetic resonance (1H NMR) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy
Summary
Metabolomics aims to measure the global, dynamic metabolic response of living complex multicellular systems to biological stimuli or genetic manipulation (Nicholson and Lindon 2008) It determines changes in low molecular weight organic metabolites in complex biological samples. In this study the LC–MS metabolomics approach is coupled to different chemometric methods, such as MCR-ALS and PLS-DA, to explore the changes observed in the metabolite profiles of S. cerevisiae when it is cultivated at different temperatures. A new strategy using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) (Tauler 1995; Pere-Trepat et al 2005) is proposed as a general approach for proper investigation and resolution of complex and extensive LC–MS data sets (in full spectral scan mode), where huge amounts of information can be uncovered, including strongly hidden coeluted and embedded unknown chromatographic peaks. Partial Least Squares-Discriminant Analysis (PLS-DA) (Barker and Rayens 2003) is applied to the MCR-ALS results to investigate what metabolites were more influenced by the temperatures changes on yeast cultures, acting as a possible biomarkers of temperature stimulus on yeast cultures
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