Complementary whole-genome technologies reveal the cellular response to proteasome inhibition by PS-341

Abstract

Although the biochemical targets of most drugs are known, the biological consequences of their actions are typically less well understood. In this study, we have used two whole-genome technologies in Saccharomyces cerevisiae to determine the cellular impact of the proteasome inhibitor PS-341. By combining population genomics, the screening of a comprehensive panel of bar-coded mutant strains, and transcript profiling, we have identified the genes and pathways most affected by proteasome inhibition. Many of these function in regulated protein degradation or a subset of mitotic activities. In addition, we identified Rpn4p as the transcription factor most responsible for the cell's ability to compensate for proteasome inhibition. Used together, these complementary technologies provide a general and powerful means to elucidate the cellular ramifications of drug treatment.

Figure 1

Effect of the proteasome inhibitor PS-341 on yeast. (a) Structures of PS-341, a dipeptidyl boronic acid, and PS-519, a β-lactone. (b) Wild-type, rpn4Δ, and pdr5Δ snq2Δ strains were grown in various concentrations of PS-341, and cell growth was monitored by absorbance at 600 nm. All three strains display a concentration-dependent inhibition of growth. (c) These three strains and a rpn4Δ pdr5Δ snq2Δ triple mutant were grown in the presence of the indicated concentrations of PS-341 for 24 h at 30°C. Absorbance at 600 nm was used to determine cell density.

Figure 2

Scatterplots of PS-341-treated samples and their appropriate controls. Normalized intensity values from untreated samples are plotted on the x axis and PS-341 treated samples on the y axis. Blue squares indicate those genes that are subunits of the 26S proteasome. Proteasome subunit genes in a given cell type have similar expression levels and are concomitantly induced in the wild-type and pdr5Δ snq2Δ strains when proteasome function is impaired. In the rpn4Δ strain, these genes have a lower level of expression in untreated cells, and this level does not change during drug treatment. Blue diagonal lines denote 2-fold changes in expression level.

Figure 3

Time, dose, and strain response to PS-341 treatment. The hierarchical clustering of the 941 genes whose level of transcription changed greater than 1.7-fold in at least 3 of the 30 experiments is presented in heat-map form. Intensity of color correlates with degree of up-regulation (red) or down-regulation (green). Numbers at top indicate concentration of PS-341 used (micromolar) and wedges represent increasing time. Essentially all proteasome subunits are in Cluster B. A tightly clustered subset of genes (outlined in yellow), down-regulated in all strains, contains genes involved in fatty acid metabolism (FAS1, FAS2, FAA1, and OLE1).

Figure 4

Population genomics identifies deletion strains impacted by proteasome inhibition. (Left) Normalized tag intensities from untreated cells compared with those from PS-341-treated (260 μM) samples at 15 generations. (Right) Fitness ratios for tags comparing the results from PS-519 (8 μM) and PS-341 (260 μM) treatments at 15 generations. In both Right and Left, UPTAG and DOWNTAG data are shown, and arrows identify tags from the rpn4Δ strain. Data points for which the tag intensity of the untreated sample was below the detection threshold have been omitted. Red and green boxes identify points for strains classified in Table 1 as hypersensitive or resistant, respectively. These were individually validated by MIC testing. The selection of a given strain for subsequent validation studies was based on data from multiple experiments. The presence of colored data points interspersed among the bulk of unaffected strain tags reflects experimental and biological variability (21).