Quantification of protein half-lives in the budding yeast proteome - PubMed (original) (raw)
Quantification of protein half-lives in the budding yeast proteome
Archana Belle et al. Proc Natl Acad Sci U S A. 2006.
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 half-life 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.
Conflict of interest statement
Conflict of interest statement: No conflicts declared.
Figures
Fig. 1.
Determination of protein half-life by using the TAP-tagged strains. (A) Schematic diagram of the translation shut-off assay used to determine the half-life of proteins in the yeast proteome. (B) The degradation rate constants of the TAP-tagged proteins were quantified by measuring the relative intensity of each protein by quantitative Western blotting at 0, 15, and 45 min after cycloheximide treatment. The intensity data were fit to a first-order decay function to estimate the degradation rate constant, which then was used to calculate a half-life. The Western blot shows degradation profiles for five representative TAP-tagged proteins. (C) Normalized distribution of the half-lives of the observed yeast proteins. The bins are log2 increments with the upper boundary indicated.
Fig. 2.
Clustering genes by similarity in protein metabolism parameters correlates with function and localization. The 3,751 yeast proteins were clustered based on protein production rate [mRNA abundance (M) x ribosome density (R)], protein abundance (P), and degradation rate constant (D). See Data Sources in Methods for details on the data sets used. Clusters with similar profiles were grouped together and graphically visualized by using colors to represent the direction and measure of the attribute (see Supporting Methods for details). A high attribute value is shown in shades of yellow, and a low attribute value is indicated with shades of blue. The black pie slices indicate the relative numbers of proteins belonging to each cluster. Clusters were analyzed for functional enrichment and localization categories. A representative list of significantly enriched GO terms is indicated in black. The entire list of significant GO terms and localization categories obtained in the analysis is described in Tables 4 and 5.
Fig. 3.
Protein half-life and production rate influence protein abundance. (A) Heat map illustrating the functional relationship between protein production rate (mRNA abundance × ribosome density; y axis), degradation rate constant (x axis), and protein abundance (color-coded). See Data Sources in Methods for details on the data sets used. Each point represents a 2D bin, including all proteins with a degradation rate constant and protein production rate in a defined range. The color of the bin represents the average abundance of the proteins contained within it. Higher values are indicated in shades of red, and lower values in shades of blue. Empty and near-empty bins are colored black. (B) Two-dimensional plot of the relationship between protein abundance and protein production (Upper) or degradation rate constant (Lower). The plots are generated by using a moving average of a window of 100 genes.
Fig. 4.
A simple model for protein metabolism. (A) Schematic of a model for protein metabolism and the corresponding steady-state prediction for protein abundance, where M is absolute mRNA abundance, P is protein concentration, R is the rate of translation per mRNA molecule (approximated by experimental data on ribosome density), D is protein degradation rate constant, and V is growth rate (volume increase factor per unit time). See Data Sources in Methods for details on the data sets used. (B) Correlation between observed and predicted protein abundance. Bar graph shows the P value (y axis) of rejecting the independence hypothesis by using the Spearman test [the Spearman rank correlations (_r_s) are indicated on the bars], between observed and predicted protein abundance, when using M, only mRNA abundance; M+D, mRNA abundance and degradation rate constant including growth correction; M+R, mRNA abundance and ribosome density; or all of the parameters described in the steady state protein metabolism equation. (C) Scatter plot showing the relationship between the observed and predicted protein abundances.
Fig. 5.
Correlation between mRNA changes and protein half-life. (A) Model for buffering protein stability differences in regulated transcription modules. Schematic of a transcription module made up of three genes with different protein degradation rate constants shown at steady state (Left) and during the transition to a repressed state (Right). The three coregulated genes produce mRNA (shown in pink) coding for proteins (in purple) of different stabilities as indicated by the different _k_deg (1 is least stable, and 3 is the most stable protein). When the genes in the module are transcriptionally repressed (Right) and the cell is aiming to maintain the same relative ratios of protein abundance as in steady state, mRNAs coding for stable proteins will be repressed more than mRNAs coding for unstable ones (indicated by the relative extent of pink filling). The same is true for cases of induction (data not shown). (B) Relationship between fold change in expression and protein half-life in the genes belonging to two transcription modules. The profile of the RNA processing module in osmotic stress (51 genes; ref. 9) (Upper) and the oxidoreductase module (44 genes; ref. 7) in response to DTT (Lower) are shown. The mRNA data come from the time point of maximum mRNA repression (for osmotic stress, 20 min) or activation (for oxidoreductase, 45 min) during time courses after a stress. The lines give the least-square fit to the data points. (C) Global correlation between protein half-life and fold change in gene expression in transcription modules. For 1,200 previously described transcription modules (10), we analyzed 27 different time courses of mRNA expression after a stress (, –14). For each module and each condition, we tested whether the magnitude of transcription induction or repression correlates with the half-life of the protein encoded by the module’s genes. To that end, we calculated the Spearman rank correlation between fold change in gene expression and protein half-life and collected all module-condition pairs for which the correlation was significant (P < 1_e_−3). The scatter plot shows the average fold change in expression (x axis) and Spearman rank correlation (y axis) for significant pairs The plot reflects distinct behaviors for induced (red) and repressed (green) modules, suggesting that, in agreement with our theoretical prediction, in cases where correlation between half-life and expression is observed, it increases the uniformity of the module’s response at the protein level.
References
- Ghaemmaghami S., Huh W. K., Bower K., Howson R. W., Belle A., Dephoure N., O’Shea E. K., Weissman J. S. Nature. 2003;425:737–741. - PubMed
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