Metabolic Profiling of Yeast Sterols Using the Agilent 7200 Series GC/Q-TOF System

Importance of the Topic

The ergosterol biosynthesis pathway in fungi represents a proven target for antifungal drug development due to its essential role in membrane integrity and its divergence from mammalian cholesterol pathways. High-resolution metabolic profiling provides an unbiased method to map pathway intermediates and identify enzymatic targets of novel inhibitors.

Objectives and Study Overview

This study combined targeted and untargeted GC/Q-TOF metabolomics in Saccharomyces cerevisiae to precisely determine the enzyme targets of known antifungal agents (Terbinafine, Fluconazole) and novel compounds (Totarol, NCE 1181-0519). The goal was to correlate drug treatment with accumulation or depletion of specific sterol intermediates.

Methodology and Instrumentation

• Sample Preparation: Wild-type yeast cultures treated with growth-inhibitory drug concentrations were harvested at mid-log phase. Lipids were extracted via Folch method, dried, and derivatized with methoxyamine hydrochloride and MSTFA + 1% TMCS.
• Instrumentation: An Agilent 7890A GC equipped with an HP-5 MS UI column (30 m × 0.25 mm, 0.25 µm) and helium carrier gas (1 mL/min) was coupled to a 7200 series GC/Q-TOF. Injection was split 20:1 at 250 °C. Oven program: 60 °C (1 min), 10 °C/min to 325 °C (3.5 min). The Q-TOF operated in EI mode, source 230 °C, quadrupole 150 °C, scan range 50–600 m/z at 5 Hz, with MS/MS at 15 V collision energy.
• Data Analysis: MassHunter Qualitative performed deconvolution (noise analysis, peak modeling, spectral deconvolution, library identification). Targeted quantification used MassHunter Quantitative; statistical filtering and volcano plots (p < 0.05, fold change > 2) were executed in Mass Profiler Professional.
• Structural Confirmation: Accurate-mass MS/MS spectra were interpreted with Molecular Structure Correlator to assign fragment formulas and validate compound identities.

Main Results and Discussion

• Terbinafine treatment led to an 84-fold accumulation of squalene, confirming ERG1 (squalene epoxidase) inhibition and downstream sterol depletion.
• Fluconazole induced a 14-fold increase in lanosterol, consistent with ERG11 (14α-demethylase) inhibition, and revealed unexpected accumulation of 14α-methyl sterols.
• Untargeted profiling of Totarol identified a >360-fold rise in 4α-carboxy-4β-methyl-5α-cholesta-8,24-dien-3β-ol, pinpointing ERG26 as its specific target.
• NCE 1181-0519 treatment caused a 6-fold increase in 4,4-dimethyl-8,24-cholestadienol and depletion of downstream intermediates, indicating ERG25 inhibition.
• The five-order-of-magnitude dynamic range and full-scan sensitivity of the GC/Q-TOF allowed simultaneous detection of low-abundance intermediates and quantitative changes across the pathway.

Benefits and Practical Applications

• Unified targeted/untargeted workflow accelerates identification of both known and novel drug targets in a single acquisition.
• Accurate-mass deconvolution resolves co-eluting sterol isomers without prior knowledge of target ions.
• Coupling metabolic profiling with genetic screening (Haploinsufficiency Profiling) enhances confidence in enzymatic target assignment.
• Approach is broadly applicable to antifungal drug discovery, pathway characterization, and quality control in industrial fermentations.

Future Trends and Opportunities

• Integration with transcriptomics and proteomics for comprehensive pathway mapping.
• Automation of deconvolution and structural annotation pipelines to increase throughput.
• Expansion to profiling of other lipid classes (sphingolipids, glycerophospholipids) across diverse organisms.
• Potential translation to clinical diagnostics for monitoring sterol biomarkers in fungal infections.

Conclusion

The Agilent 7200 GC/Q-TOF platform combined with advanced software deconvolution and structural correlation offers a robust, high-resolution method for metabolic profiling of yeast sterols. This workflow confidently identified both established and novel antifungal targets (ERG1, ERG11, ERG25, ERG26) and can be extended to broader lipidomics applications.

References

• Giaever G, Flaherty P, Kumm J, Proctor M, Nislow C, Jaramillo DF, Chu AM, Jordan MI, Arkin AP, Davis RW. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc Natl Acad Sci U S A. 2004;101(2):793–798.
• Folch J, Lees M, Stanley GH. A simple method for the isolation and purification of total lipids from animal tissues. J Biol Chem. 1957;226(1):497–509.