Delayed correlation of mRNA and protein expression in rapamycin-treated cells and a role for Ggc1 in cellular sensitivity to rapamycin

Abstract

To identify new molecular targets of rapamycin, an anticancer and immunosuppressive drug, we analyzed temporal changes in yeast over 6 h in response to rapamycin at the transcriptome and proteome levels and integrated the expression patterns with functional profiling. We show that the integration of transcriptomics, proteomics, and functional data sets provides novel insights into the molecular mechanisms of rapamycin action. We first observed a temporal delay in the correlation of mRNA and protein expression where mRNA expression at 1 and 2 h correlated best with protein expression changes after 6 h of rapamycin treatment. This was especially the case for the inhibition of ribosome biogenesis and induction of heat shock and autophagy essential to promote the cellular sensitivity to rapamycin. However, increased levels of vacuolar protease could enhance resistance to rapamycin. Of the 85 proteins identified as statistically significantly changing in abundance, most of the proteins that decreased in abundance were correlated with a decrease in mRNA expression. However, of the 56 proteins increasing in abundance, 26 were not correlated with an increase in mRNA expression. These protein changes were correlated with unchanged or down-regulated mRNA expression. These proteins, involved in mitochondrial genome maintenance, endocytosis, or drug export, represent new candidates effecting rapamycin action whose expression might be post-transcriptionally or post-translationally regulated. We identified GGC1, a mitochondrial GTP/GDP carrier, as a new component of the rapamycin/target of rapamycin (TOR) signaling pathway. We determined that the protein product of GGC1 was stabilized in the presence of rapamycin, and the deletion of the GGC1 enhanced growth fitness in the presence of rapamycin. A dynamic mRNA expression analysis of Deltaggc1 and wild-type cells treated with rapamycin revealed a key role for Ggc1p in the regulation of ribosome biogenesis and cell cycle progression under TOR control.

Fig. 1.

Large scale quantitative proteomics analysis of yeast response to rapamycin. A, STNs of proteins significantly changing in abundance at a threshold p value of 0.01 at each time point of rapamycin treatment (0, 20, 40, 60, 120, 240, and 360 min). STNs were calculated from treated over untreated data sets. Values ranging from −3 to +3 are gradually colored from blue to black to yellow as indicated by the scale and clustered using Euclidian distance using the HCA function in MeV 4.0. B and C, average NSAF values and S.D. (calculated from at least three replicated experiments) in rapamycin-treated (y axis) over untreated (x axis) conditions for the 29 proteins whose abundance decreased (B) and 56 proteins whose abundance increased (C) at 0, 120, 240, and 360 min of rapamycin treatment. The red line defines an expression ratio of 1 or unchanged protein abundance. Each dot represents single protein identification. Proteins located above or below the red line are considered either more or less abundant in response to rapamycin treatment, respectively. Data points are represented as the average +/− the standard deviation of the four biological replicates.

Fig. 2.

Cellular functions of proteins involved in rapamycin response. Proteins that were significantly changing in abundance at a p value threshold of 0.01 are sorted by biological process (GO terms) (A) and cellular localization (B) (Yeast GFP (green fluorescent protein) Fusion Localization Database). Proteins decreasing and increasing in abundance are colored in blue and yellow, respectively. In both panels, the number of proteins is shown on the y axis, and either biological process or cellular localization is shown on the x axis.

Fig. 3.

Temporal comparison of mRNA and protein abundance changes in response to rapamycin. A, colored map of the ratios of the 328 mRNA and protein expression gene pairs identified at each time point of rapamycin treatment. mRNA expression ratios were calculated from the Cy3/Cy5 intensity obtained from microarray data. Protein ratios were calculated from ratios of NSAF treated over untreated. Ratios values were log₂-transformed and clustered in MeV 4.0 using the HCA function with Euclidean distance and average linkage to consolidate trends across time points. Values ranging from −3 to +3 are gradually colored from blue to yellow as indicated by the scale. B, Pearson correlation coefficients calculated between mRNA and protein expression ratios at each time and in a time-shifted manner of the 328-gene data set. Values are gradually colored from blue to yellow as the Pearson correlation coefficient increases. C, patterns of correlation between mRNA and protein abundance changes over the time course of rapamycin treatment for ribosomal proteins (group 4); heat shock, retrograde response, and proteolysis proteins (group 1), tricarboxylic acid cycle enzymes involved in the retrograde response, vesicle-mediated transporter, and DNA metabolism proteins (group 3); and drug exporter (group 6). These patterns belong to the 12 patterns defined in supplemental Fig. 4 and Table 4B. Log₂ ratio expression is illustrated in dashed lines for mRNA and black lines for proteins.

Fig. 4.

Number of proteins and their localization within different patterns of correlation between mRNA and protein abundance changes. A, the number of proteins identified with the 85 proteins identified from the PLGEM analysis (y axis) were sorted by pattern of mRNA and protein abundance changes (x axis) in which mRNA is down-protein is down, mRNA is up-protein is up, mRNA is up-protein is not changing, mRNA is down-protein is not changing, mRNA is down-protein is up, and mRNA is not changing-protein is up. B, localization of proteins belonging to patterns of correlation between mRNA and protein abundance changes in which mRNA is down-protein is down (blue), mRNA is up-protein is down (green), mRNA is down-protein is up (orange), mRNA is not changed-protein is up (red), and mRNA is up-protein is up (dark red). Prot, protein; down, decreasing in expression or abundance; up, increasing in expression or abundance; no change, no change in expression or abundance.

Fig. 5.

Integration of expression changes patterns with functional genomics analysis and Ggc1 protein stability analysis. A, growth of deletion strains significantly affected in their growth in response to rapamycin at a p value threshold of 0.1. Growth fitness was compared at six different dilutions A–F where A was undiluted and B–F ranged from 10⁻² to 10⁻⁶ dilution factors starting with an optical density at 600 nm between 0.8 and 1.2 (10-fold dilution series from A–F). Two technical replicates of each dilution are shown in each panel A–F. B, correlation of mRNA and protein abundance changes and deletion strain phenotype of the 85 proteins significantly changing in abundance by PLGEM at a p value threshold of 0.01. The mRNA and protein expression ratios were compared with the -fold change difference in growth between deletion strain and wild-type strain (between rapamycin-treated and untreated conditions). Values were clustered using the HCA function in MeV 4.0 by Pearson correlation. mRNA and protein decreasing, increasing, or not changing in abundance are illustrated in blue, yellow, and black, respectively. Deletion strains sensitive to, resistant to, or not affected by rapamycin treatment (compared with the wild-type strain BY4741) are illustrated in blue, yellow, and black, respectively. Genes highlighted in red were statistically significant in difference of growth fitness compared with the wild type in the presence of rapamycin. Note that the expression changes are measured by a ratio. Therefore, a decrease in expression is represented by a ratio below 1. C, Ggc1-TAP was grown to an A₆₀₀ = 0.6, and cells were treated for the indicated time in the presence of 35 μg/ml cycloheximide ±100 nm rapamycin. Cell extracts from each time point were analyzed by Western blot analysis for Ggc1-TAP and Pgk1 (indicated with arrows).

Fig. 6.

Gene expression change analysis of wild-type and Δggc1 cells in response to rapamycin treatment. Gene expression changes were analyzed between wild-type and Δggc1 cells treated with or without rapamycin (100 nm) at 0, 1, 2, and 4 h. The figure shows a colored map of log₂ expression ratios from genes significantly changing in ggc1 mutant compared with wild type in the presence of rapamycin. Values ranging from −3 to +3 are gradually colored from blue to black to yellow as indicated by the scale and clustered using Euclidian distance using the HCA function in MeV. Genes that were up-regulated were ribosomal genes or genes involved in ribosome biogenesis, whereas down-regulated genes were mostly involved in cell cycle progression, mitosis, and meiosis. ECCB, early cell cycle box.