Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe

Michael W Schmidt^1,2,3, Andres Houseman^4,a, Alexander R Ivanov^1,2 & Dieter A Wolf^1,2

^aPresent Address: Department of Work Environment, University of Massachusetts Lowell, One University Avenue, Lowell, MA 01854, USA.

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Synopsis

The unicellular archiascomycete fungus Schizosaccharomyces pombe is a well-established model organism, but only 1500 of its predicted 4900 genes and proteins have been experimentally characterized. Weighing the advantages and disadvantages of currently available methods for quantitative proteomics, we have embarked on a mass spectrometry-based approach for relative quantitation of native, unmodified fission yeast proteins. In addition, we have compared mRNA and protein expression profiles in fission yeast and budding yeast to assess the overall protein–mRNA correlation in these related organisms.

We devised an extensive multidimensional biochemical prefractionation scheme of total cell lysate from wild-type fission yeast cells, followed by analysis of individual fractions by liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC ESI MS/MS). Approximately 3 million mass spectra were matched to 12 413 non-redundant peptides, resulting in the identification of 1465 proteins (29.5% of the predicted fission yeast proteome). The list of proteins was representative of the whole proteome across the entire range of molecular weights, isoelectric points and gene ontology (GO) attributes. More detailed analysis revealed equal identification rates for essential and non-essential proteins (both 36%). Similarly, yeast-specific proteins were represented at the same rate as the entire proteome (30%). Metazoan 'core' proteins were overrepresented (47%), whereas we undersampled proteins containing predicted transmembrane domains (14%) and S. pombe-specific proteins (10%).

To quantitatively rank the identified proteins relative to each other we used spectral counts (Liu et al, 2004; Kislinger et al, 2006). A negative binomial regression model was developed to adjust spectral counts to the number of predicted tryptic peptides allowing one miscleavage. Based on adjusted spectral counts (ASCs), we assembled an abundance ranked list of all 1465 proteins identified, which was validated by comparing it to absolute quantitation data established for a series of cytokinesis-related fission yeast proteins (Wu and Pollard, 2005). Plotting our ASC data versus the absolute quantitation data revealed a close correlation (r_P=0.98), suggesting that ASCs provide a good approximation of relative protein abundance.

The range of ASCs spanned more than three orders of magnitude. The mean ASC was 68.0, whereas the median was 14.6, indicating that the vast majority of the 1465 proteins identified are of relatively low abundance. The median of metazoan core proteins (ASC=24.2) is significantly higher than that of all proteins detected, whereas the abundance of S. pombe-specific proteins is considerably lower (ASC=5.5). In addition, essential proteins are considerably more abundant (median ASC=12.6) than non-essential proteins (ASC=7.5). This finding can be rationalized by the enrichment of highly expressed core proteins in the set of essential proteins (Supplementary Data File 2). Analysis of 10 protein complexes for which we identified greater than 80% of their known or predicted subunits indicated that the protein synthesis machinery (ribosome, eIFs) and also the protein folding and degradation machinery (CCT chaperonin, proteasome) are among the most abundant molecular modules in fission yeast (Figure 2D).

Figure 2

Label-free relative quantification of S. pombe proteins. (A) Correlation of published absolute quantitation data for several cytokinesis proteins with their corresponding ASCs. (B) ASCs for each of the 1465 identified proteins plotted on a log scale. (C) Median ASCs for proteins belonging to the indicated categories. All numbers were statistically different at P<0.05 (TMD=transmembrane domain). (D) Median ASCs for subunits of the indicated protein complexes. For protein complexes with few subunits, P-values are not always <0.05 owing to some outliers (see Supplementary Data File 4). (E) Correlation between the Pombe-ASC and the Cerevisiae-ASC data sets.

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We also determined the overall correlation of our protein data set with mRNA abundance as estimated by cDNA microarray analysis. The comparison of 1367 protein–mRNA pairs revealed a Pearson correlation coefficient (r_P) of 0.58 (Figure 3A), indicating a substantial correlation between mRNA and protein abundance in fission yeast similar to what was found for budding yeast (Ghaemmaghami et al, 2003; Greenbaum et al, 2003). We also calculated correlation coefficients for specific functional pathways, protein families and multisubunit protein complexes. Whereas high coefficients were obtained for signalling and metabolic pathways (Figure 3B), the majority of multisubunit protein complexes showed very low or even negative correlation coefficients (Figure 3B). The poor protein–mRNA correlation for complexes would be expected, if their subunits were coordinately regulated. Coordinate regulation may lead to clustering of protein–mRNA ratios for complex components around a similar value, thus precluding a strong correlation. Indeed, we noticed more consistent protein–mRNA ratios for individual complex subunits than observed for all proteins. Thus, while the protein–mRNA correlations were low for multisubunit protein complexes, clustering of their protein–mRNA ratios around similar values indicated coordinate regulation of complex subunits (Table I). Although this regulation could principally occur at any level, the low protein–mRNA correlation suggests a substantial contribution of post-transcriptional mechanisms (Greenbaum et al, 2003).

Figure 3

Correlation of protein and mRNA levels in fission yeast. (A) Scatter plot representing the relationship between mRNA and protein (Pombe-ASC). The Pearson correlation coefficient is indicated. (B) Protein–mRNA correlation coefficients for proteins belonging to the indicated pathways, protein families, and complexes. Dashed lines indicate 95% confidence intervals (AA=amino acid, UPR=unfolded protein response, TCA=tricarboxylic acid cycle). (C) Protein–mRNA ratios for individual members of the indicated pathways, protein families, or complexes. The data are displayed relatively to median centered ratios of the entire data set of 1381 mRNA–protein pairs (black graphs).

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Table 1: Protein–mRNA relationships

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The reverse scenario, clustering of protein–mRNA ratios around similar values, but relatively high protein–mRNA correlation, was observed for the stress response pathway as well as for glycolysis and amino acid biosynthesis (Figure 3B and C). This pattern might reflect the fact that proteins involved in hierarchical signal transduction cascades or metabolic pathways do not necessarily cooperate in stoichiometric amounts. Most other pathways and protein families showed no clustering. Among those were entities with both low (transporters, Figure 3B) and high (kinases; Figure 3B) protein–mRNA correlation. For these remaining cases, high protein–mRNA correlations would suggest control primarily at the transcriptional level, whereas low correlations would indicate extensive posttranscriptional control (Table I) (Greenbaum et al, 2003).

The generation of quantitative fission yeast protein and mRNA data sets and the availability of corresponding data sets for budding yeast enabled the first large-scale comparison of mRNA and protein levels of two eukaryotic organisms. Self-organizing map clustering SOM revealed many similarities in the mRNA and protein abundance patterns in the two yeasts, but also marked differences. Many SOM clusters were significantly enriched for non-redundant GO attributes (P0.0005). This finding suggests that many pathways and complex subunits are coordinately, albeit not necessarily concordantly regulated in both fission and budding yeasts. For example, 6/13 components of the microtubule cytoskeleton organization GO category present in our data sets were coordinately and concordantly regulated in both yeasts (cluster 10; Figure 5B). In contrast, ATPases and entities involved in chromatin remodelling and intracellular transport were coordinately, but discordantly regulated with mRNA levels being low in budding yeast (cluster 14, Figure 5C–E). Our comparison reinvigorates the conclusion gained from previous functional genomics studies that similarities in the control of gene expression in the two yeasts are less pronounced than expected from genome comparisons (Mata et al, 2002; Oliva et al, 2005; Rustici et al, 2004).

Figure 5

Comparison of proteome and transcriptome data from S. pombe and S. cerevisiae. (A) Self-organizing map cluster analysis of the fission and budding yeast mRNA and ASC protein data sets. The table on the right shows GO terms overrepresented in the various clusters and the P-values of enrichment. Also indicated is the number of proteins with a particular GO attribute enriched in each cluster over the total number with this attribute present in the entire data set. The names of the genes and proteins in the individual clusters are listed in Supplementary Data File 6. (B–E) Detailed view of subclusters containing (B) microtubule cytoskeleton organization (GO: 0000226), (C) chromatin modification components (GO: 0016568), (D) components involved in intracellular transport (GO: 0046907), (E) ATPases (GO: 0016887), and (F) ribosomal proteins (GO: 0005830). The graphs next to the heat maps indicate the mean variations in signal intensities.

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