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Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS.

Farrés M, Piña B, Tauler R - Metabolomics (2014)

Bottom Line: 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.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 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain.

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.

No MeSH data available.


Variables importance in projection (VIP scores) plot resulting from PLS-DA analysis of the autoscaled chromatographic peak areas obtained by MCR-ALS analysis of yeast samples. Horizontal red line shows the threshold value selecting the most important variables (Color figure online)
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Fig4: Variables importance in projection (VIP scores) plot resulting from PLS-DA analysis of the autoscaled chromatographic peak areas obtained by MCR-ALS analysis of yeast samples. Horizontal red line shows the threshold value selecting the most important variables (Color figure online)

Mentions: VIP values (Eriksson et al. 2006) calculated from PLS-DA model (see Sect. 3.3) revealed what variables (metabolites) were more important to discriminate the effects of temperature changes on yeast metabolism. Variables whose VIP values were higher than 1.0 were considered as potential indicators (Chong and Jun 2005) of these effects (see Fig. 4). Results are listed in Table 2. PLS-DA VIPs (Fig. 4) and the corresponding PLS-DA weights provided a summary of PLS-DA results. In this table chromatographic peaks increasing (‘Ups’) or decreasing (‘Downs’) at 42 ºC relative to the control samples cultured at 30 ºC are given.Fig. 4


Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS.

Farrés M, Piña B, Tauler R - Metabolomics (2014)

Variables importance in projection (VIP scores) plot resulting from PLS-DA analysis of the autoscaled chromatographic peak areas obtained by MCR-ALS analysis of yeast samples. Horizontal red line shows the threshold value selecting the most important variables (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4289532&req=5

Fig4: Variables importance in projection (VIP scores) plot resulting from PLS-DA analysis of the autoscaled chromatographic peak areas obtained by MCR-ALS analysis of yeast samples. Horizontal red line shows the threshold value selecting the most important variables (Color figure online)
Mentions: VIP values (Eriksson et al. 2006) calculated from PLS-DA model (see Sect. 3.3) revealed what variables (metabolites) were more important to discriminate the effects of temperature changes on yeast metabolism. Variables whose VIP values were higher than 1.0 were considered as potential indicators (Chong and Jun 2005) of these effects (see Fig. 4). Results are listed in Table 2. PLS-DA VIPs (Fig. 4) and the corresponding PLS-DA weights provided a summary of PLS-DA results. In this table chromatographic peaks increasing (‘Ups’) or decreasing (‘Downs’) at 42 ºC relative to the control samples cultured at 30 ºC are given.Fig. 4

Bottom Line: 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.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 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain.

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.

No MeSH data available.