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American Chemical Society, Analytical Chemistry, 7(87), p. 4008-4014, 2015

DOI: 10.1021/acs.analchem.5b00264

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Feasibility of Protein Turnover Studies in Prototroph Saccharomyces cerevisiae Strains

Journal article published in 2015 by Miguel Martin-Perez, Judit Villen ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Quantitative proteomics studies of yeast that use metabolic labeling with amino acids rely on auxotrophic mutations of one or more genes on the amino acid biosynthesis pathways. These mutations affect yeast metabolism, and preclude the study of some biological processes. Overcoming this limitation, it has recently been described that proteins in a yeast prototrophic strain can also be metabolically labeled with heavy amino acids. However, the temporal profiles of label incorporation under the different phases of the prototroph's growth have not been examined. Labeling trajectories are important in the study of protein turnover and dynamics, in which label incorporation into proteins is monitored across many timepoints. Here we monitored protein labeling trajectories for 48 h after a pulse with heavy lysine in a yeast prototrophic strain and compared them with those of a lysine auxotrophic yeast. Labeling was successful in prototroph yeast during exponential growth phase but not in stationary phase. Furthermore, we were able to determine the half-lives of more than 1,700 proteins during exponential phase of growth with high accuracy and reproducibility. We found a median half-life of 2 h in both strains, which corresponds with the cellular doubling time. Nucleolar and ribosomal proteins showed short half-lives whereas mitochondrial proteins and other energy production enzymes presented longer half-lives. Except for some proteins involved in lysine biosynthesis, we observed a high correlation in protein half-lives between prototroph and auxotroph strains. Overall, our results demonstrate the feasibility of using prototrophs for proteomic turnover studies and provide a reliable dataset of protein half-lives in exponentially growing yeast.