Proteomics in Multi-omics Workflows Using Yeast as a Model System

Importance of the Topic

Mapping proteomics data onto biological pathways accelerates target identification and enhances interpretation of complex datasets in systems biology. Integrating proteomics with other omics layers supports a holistic view of metabolic and regulatory networks, guiding focused experiments and validating discovery findings.

Goals and Overview of the Study

The study demonstrates two complementary workflows using Saccharomyces cerevisiae as a model:

• A discovery-driven proteomics pipeline to identify differential proteins under chemical and ionic stress
• A metabolomics-guided targeted proteomics strategy to quantify proteins in specific pathways

By mapping discovery results to curated yeast pathways and then directing a targeted assay based on metabolite perturbations, the approach illustrates an integrated multi-omics analysis.

Methodology and Used Instrumentation

Yeast strain BJ5459 was cultured and exposed to cyclosporin A, FK506, calcium stress or vehicle control. Cells were harvested, quenched, lyophilized, bead-beaten with glycerol buffer, and digested via the FASP protocol. Discovery runs used an Agilent 6550 iFunnel Q-TOF with a microfluidic HPLC-Chip over 120-minute gradients in data-dependent acquisition mode. Targeted assays employed an Agilent 6490 Triple Quadrupole in dynamic MRM mode with 90-minute gradients. Data processing combined Agilent Spectrum Mill for peptide–protein identification, Skyline for MRM optimization, and Mass Profiler Professional with Pathways Architect for statistical evaluation and pathway mapping.

Used Instrumentation

• Agilent 1200 Series HPLC-Chip (360 nL enrichment, 150 mm×75 µm C18)
• Agilent 6550 iFunnel Q-TOF MS (extended dynamic range)
• Agilent 6490 Triple Quadrupole MS (dynamic MRM)
• Spectrum Mill, Skyline, Mass Profiler Professional, Pathways Architect software

Main Results and Discussion

The discovery workflow identified 3 446 proteins (1.2 % spectral FDR) and 13 616 unique peptides. PCA showed clear separation of treatment groups. Pathway mapping highlighted down-regulation of ergosterol biosynthesis under FK506 stress. The targeted workflow achieved full coverage of 15 proteins from three salvage and nucleotide biosynthesis pathways, compared to only three proteins detected in the untargeted approach. Dynamic MRM provided sensitive quantitation, and multi-omic visualization in MPP displayed coordinated changes in metabolites and proteins on the adenine/hypoxanthine salvage pathway.

Benefits and Practical Applications

• Pathway mapping focuses discovery on biologically relevant targets
• Metabolomics-directed assays increase coverage and throughput
• Dynamic MRM enables rapid and sensitive quantitation of pathway proteins
• Multi-omic integration supports comprehensive systems biology investigations

Future Trends and Potential Applications

Advances in software for automated pathway-based assay design and deeper integration of genomics, proteomics, and metabolomics will enhance precision medicine, biomarker discovery, and metabolic engineering. Emerging ion mobility and high-field MS technologies promise further gains in sensitivity and throughput.

Conclusion

Combining pathway mapping with discovery and targeted proteomics in a multi-omics framework streamlines hypothesis generation and validation. The demonstrated workflows in yeast underscore the power of integrating metabolite and protein data for focused, high-coverage analysis of critical biological processes.

Reference

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