Magnetic Resonance Mass Spectrometry (MRMS) discriminates yeast mutants through metabolomics

Importance of the Topic

Understanding subtle metabolic changes in organisms at the single-gene level is critical for functional genomics and biotyping. Untargeted metabolomics using Magnetic Resonance Mass Spectrometry (MRMS) provides ultra-high mass resolution and accuracy, enabling the detection of intricate metabolite variations that other methods may miss. Saccharomyces cerevisiae serves as a model eukaryote for exploring pathways like methylglyoxal detoxification, which has broad implications in stress biology and toxicology.

Study Objectives and Overview

The main goal was to assess whether an untargeted MRMS aXelerate workflow could distinguish phenotypically identical single-gene deletion mutants of S. cerevisiae affecting methylglyoxal catabolism. The study compared three deletion strains (∆GLO1, ∆GLO2, ∆GRE3) and a reference strain under identical growth conditions. Metabolic profiles were generated and analyzed to unveil discriminating biomarkers and pathway alterations.

Methodology and Instrumentation

Yeast strains were cultivated in YPD medium to late exponential phase. Metabolites were extracted with cold methanol/water, clarified by centrifugation, and diluted with formic acid and an internal lock mass standard. Direct infusion MRMS analysis was performed using a solariX XR 7 T instrument equipped with an Apollo II electrospray source in positive ion mode. Spectra were acquired at a resolution of 1 000 000 (m/z 400) over 200–1 200 m/z. Data processing included peak alignment and annotation via MetaboScape 4.0 with T-ReX 2D, SmartFormula for formula prediction, and database matching against YMDB and HMDB. Multivariate analyses (PCA, HCA, PLS-DA) were applied with Pareto scaling and VIP scoring to identify discriminant metabolites.

Main Results and Discussion

A total of 21 174 features were detected, with 624 annotated via HMDB and 3 943 assigned by molecular formula. PCA and hierarchical clustering achieved clear separation of all four strains, grouping ∆GLO1 and ∆GLO2 apart from ∆GRE3 and the reference. PLS-DA highlighted glutathione as the top discriminant compound, confirmed by detection of its isotopologues in high resolution. Reduced glutathione levels correlated with impaired glyoxalase activity, offering insight into pathway compensation and oxidative stress responses. Additional metabolites in the glutathione pathway also varied in abundance, reflecting altered detoxification capacity.

Benefits and Practical Applications of the Method

• High discrimination power enables identification of isogenic strains differing by a single gene
• Non-targeted approach captures a broad spectrum of metabolite changes
• Potential for rapid microbial biotyping in research and quality control
• Enhanced sensitivity to pathway perturbations supports functional genomics studies

Future Trends and Potential Applications

Advances in MRMS instrumentation and workflow automation will expand untargeted metabolomics to diverse microorganisms and complex biological systems. Integration with comprehensive metabolome databases and machine learning will further improve annotation confidence and predictive modeling. Real-time monitoring of metabolic fluxes and adaptation of high-throughput data pipelines promise broader adoption in industrial biotechnology, clinical diagnostics, and environmental monitoring.

Conclusion

This study demonstrates that untargeted MRMS metabolomics can effectively distinguish phenotypically identical single-gene deletion yeast strains by revealing subtle metabolic shifts in methylglyoxal catabolism. The approach highlights glutathione dynamics as a key biomarker and underscores the potential of extreme mass resolution methods for microbial discrimination and pathway analysis.

References

• Gomes RA, Sousa Silva M, Vicente Miranda H, Ferreira AE, Cordeiro CAA, Freire AP (2005). Protein glycation in Saccharomyces cerevisiae. The FEBS Journal 272:4521-4531.
• Kuepfer L, Sauer U, Blank LM (2005). Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Research 15(10):1421-1430.
• Ramirez-Gaona M, Marcu A, Pon A, Guo AC, Sajed T, Wishart NA, Karu N, Feunang YD, Arndt D, Wishart DS (2017). YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Research 45(D1):D440-D445.
• Sousa Silva M, Gomes RA, Ferreira AE, Ponces Freire A, Cordeiro C (2013). The glyoxalase pathway: the first hundred years and beyond. Biochemical Journal 453(1):1-15.
• Vander Jagt DL, Hunsaker LA (2003). Methylglyoxal metabolism and diabetic complications: roles of aldose reductase, glyoxalase-I, betaine aldehyde dehydrogenase and 2-oxoaldehyde dehydrogenase. Chemical Biology Interactions 143-144:341-351.
• Wagner A (2000). Robustness against mutations in genetic networks of yeast. Nature Genetics 24(4):355-361.
• Winzeler EA et al. (1999). Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285(5429):901-906.
• Wishart DS et al. (2007). HMDB: the Human Metabolome Database. Nucleic Acids Research 35(D):D521-D526.