Cell-Free Identification of S. cerevisiae Strains by Analysis of Extracellular Matrices using Liquid Chromatography-Mass Spectrometry
Authors:   , Muste Cathy A. , Owens Kevin Glenn 
Published: July 2021

Current literature shows a gap for the identification of yeast strains in samples where there are no viable cells remaining. The studies contained in this thesis describe the development of a cell-free technique to identify strains of S. cerevisiae using Liquid Chromatography-Mass Spectrometry (LC-MS) by analysis of the yeast supernatant and beer. Solid phase extraction (SPE) was used to concentrate analytes while removing sugars, salts, and polar small molecule metabolites, thus improving peptide detection and reducing potential ion suppression. SPE also served to remove highly hydrophobic cellular components, such as lipids, reducing interference with peptides and system contamination. Non-targeted analysis was performed and the data were simplified into "pseudo-spectra" for classification using spectral pattern matching (k-nearest neighbors), and biomarker matching. MS/MS fragmentation was also acquired using data-dependent acquisition (DDA) to identify specific peptides and proteins. This provided additional information about the sample composition and stability, without the need for additional sample preparation or additional instrument time. Yeast grown in various media, and in various stages of growth, were tested to examine the robustness of the technique. Similarly, beers at various ages, and from separate batches, were evaluated. 100% of pure yeast supernatant samples were successfully classified according to strain by specific biomarker identification/detection, and by MS pattern recognition. The ability to classify the samples was not affected by incubation time (2 - 7 days) or by the carbon source used in the growth media (glucose, maltose, or fructose). However, longer incubation times, plus the addition of nutrients (nitrogen, trace metals, vitamins, and salts), did increase the amount of proteins and peptides identified, enabling the generation of more detectable biomarkers. 80% of beer samples were also successfully classified according to the yeast strain (4 of 5), when compared against different batches of beer using the same yeast strains. In this case, biomarkers were more useful than pattern recognition. This may be due to the large number of analytes in the patterns that are attributable to partially digested barley. An additional complication was uncovered during the analysis of beer. The composition of the beer peptides change over time, as the samples age from 0 to 5 months; the number of detectable peptides and proteins decrease, and the average peptide size decreases. This indicates that active proteases/peptidases remain in the beer, as the degradation rate is far higher than what is observed in typical aqueous environments. Additional batch-to-batch, and bottle-to-bottle differences in the beer manufacturing process were evident. Finally, it was attempted to classify beer samples using biomarkers identified in pure yeast supernatant samples. This was not successful. There is potential that this could be re-visited in the future, if improvements to the biomarker identification strategy occur. Improved sample cleanup of non-peptide compounds, and the inclusion of chromatographic retention time in the identification of biomarkers, would increase the likelihood of success. This technique could potentially be used for quality control of various fermented foods or beverages, as well as for the analysis of environmental samples where yeast strain identification would be valuable. It could likely be expanded for strain identification of other types of microbial samples, such as bacteria.