Quantification of protein half-lives in the budding yeast proteome

Abstract

A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the halflife of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.degradation ͉ proteomics ͉ cycloheximide ͉ epitope-tagged T he availability of whole-genome sequences and the advent of microarray technology have made global analyses of mRNA expression mainstream. However, most biological processes are mediated by proteins, which are subject to posttranscriptional regulation that is generally not observable at mRNA levels. A complete understanding of biological systems requires knowledge of protein properties, which is ultimately the goal of proteomics.Despite tremendous technical advances and effort in proteomics, the chemical heterogeneity of proteins and the large dynamic range of protein abundance make it challenging to establish global proteomic assays. This obstacle has been circumvented in the yeast Saccharomyces cerevisiae with the availability of two collections of yeast strains expressing epitope-tagged fusion proteins, one by using the tandem affinity purification (TAP) tag and a second employing the GFP (1, 2). In an initial study, we analyzed the TAP-tagged strain collection by Western blotting to quantify steady-state levels of protein abundance in actively dividing yeast cells. These data augmented previous efforts to quantify protein abundance by using mass spectrometry and 2D gel electrophoresis and provided a more comprehensive estimate of protein levels in a eukaryotic cell (3, 4).The availability of high-throughput protein abundance data has facilitated analysis of the relationship between protein abundance and mRNA levels. Although a statistically significant correlation is observed between these parameters, individual genes with similar mRNA levels can produce proteins with very different abundances. This complication makes it difficult to extrapolate from mRNA levels and microarray experiments to protein abundance. Three potential...

Implementing Arithmetic and Other Analytic Operations by Transcriptional Regulation

“…The ''fast'' genes were given protein half-lives of 5 minutes and the ''slow'' genes were given protein half lives of 10 hours. These values are somewhat extreme, but the short half-life is consistent with measurements for actively degraded proteins [54] and both halflives are within the range of recent measurements in a genomewide study of yeast [55]. All other parameters are in typical ranges.…”

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

“…Workload was calculated using the same approach reported previously 8, 32 that presumes that in steady state the net change in protein concentration is zero, and hence we can estimate the synthesis rate of individual substrates using known protein half‐lives 51. Using protein abundances reported in PaxDB 13, the workload equates to a folding flux (number of molecules synthesized per min), which constitutes the work to be met by the chaperone pool.…”

Single yeast cells vary in transcription activity not in delay time after a metabolic shift

“…As long as mRNA fluctuations occur on a fast enough timescale such that they do not propagate to protein levels, protein function, which contributes to fitness, is not compromised and natural selection will likely not punish fluctuations in mRNA levels. This we expect to be very common, as the timescales of transcription bursting 12 and the lifetimes of mRNAs 53 are typically much shorter than the response time of metabolic enzymes, which tend to have characteristic times close to the generation time 14,53 .…”