Global Comparative Label-Free Yeast Proteome Analysis by LC-MS/MS After High-pH Reversed-Phase Peptide Fractionation Using Solid-Phase Extraction Cartridges.

Discovery-driven comparative proteomics employing the bottom-up strategy with label-free quantification on high-resolution mass analyzers like an Orbitrap in a hybrid instrument has the capacity to reveal unique biological processes in the context of plant metabolic engineering. However, proteins are very heterogeneous in nature with a wide range of expression levels, and overall coverage may be suboptimal regarding both the number of protein identifications and sequence coverage of the identified proteins using conventional data-dependent acquisitions without sample fractionation before online nanoflow liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS). In this chapter, we detail a simple and robust method employing high-pH reversed-phase (HRP) peptide fractionation using solid-phase extraction cartridges for label-free proteomic analyses. Albeit HRP fractionation separates peptides according to their hydrophobicity like the subsequent nanoflow gradient reversed-phased LC relying on low pH mobile phase, the two methods are orthogonal. Presented here as a protocol with yeast (Saccharomyces cerevisiae) as a frequently used model organism and hydrogen peroxide to exert cellular stress and survey its impact compared to unstressed control as an example, the described workflow can be adapted to a wide range of proteome samples for applications to plant metabolic engineering research.

GC-MS/MS Profiling of Plant Metabolites.

Gas chromatography coupled to electron ionization (EI) quadrupole mass spectrometry (GC-MS) is currently one of the most developed and robust metabolomics technologies. This approach allows for simultaneous measurements of large number of chemically diverse compounds including organic acids, amino acids, sugars, sugar alcohols, aromatic amines, and fatty acids. Untargeted GC-MS profiling based on full scan data acquisition requires complicated raw data processing and sometime provides ambiguous metabolite identifications. Targeted analysis using GC-MS/MS can provide better specificity, increase sensitivity, and simplify data processing and compound identification but wider application of targeted GC-MS/MS approach in metabolomics is hampered by the lack of extensive databases of MRM transitions for non-derivatized and derivatized endogenous metabolites. The focus of this chapter is the automation of GC-MS/MS method development which makes it feasible to develop quantitative methods for several hundred metabolites and use this strategy for plant metabolomics applications.

Comparative Proteomics Analysis Reveals Unique Early Signaling Response of Saccharomyces cerevisiae to Oxidants with Different Mechanism of Action.

The early signaling events involved in oxidant recognition and triggering of oxidant-specific defense mechanisms to counteract oxidative stress still remain largely elusive. Our discovery driven comparative proteomics analysis revealed unique early signaling response of the yeast Saccharomyces cerevisiae on the proteome level to oxidants with a different mechanism of action as early as 3 min after treatment with four oxidants, namely H2O2, cumene hydroperoxide (CHP), and menadione and diamide, when protein abundances were compared using label-free quantification relying on a high-resolution mass analyzer (Orbitrap). We identified significant regulation of 196 proteins in response to H2O2, 569 proteins in response to CHP, 369 proteins in response to menadione and 207 proteins in response to diamide. Only 17 proteins were common across all treatments, but several more proteins were shared between two or three oxidants. Pathway analyses revealed that each oxidant triggered a unique signaling mechanism associated with cell survival and repair. Signaling pathways mostly regulated by oxidants were Ran, TOR, Rho, and eIF2. Furthermore, each oxidant regulated these pathways in a unique way indicating specificity of response to oxidants having different modes of action. We hypothesize that interplay of these signaling pathways may be important in recognizing different oxidants to trigger different downstream MAPK signaling cascades and to induce specific responses.