In this study, we exploited the power of SRM-based targeted proteomics to consistently and reproducibly detect and quantify a target set of yeast metabolic proteins covering a broad range of abundance levels across several samples and experiments. From the 228 target protein set, selected from a consensus stochiometric metabolic model for the central carbon and amino-acid metabolism of S. cerevisiae
, ~90% were successfully identified in minimally one of the samples. This substantially expands the coverage achieved by previous proteomic studies of yeast metabolism. For example, 57, 58 and 55% of the here targeted metabolic proteome was covered by de Groot et al (2007)
, Gutteridge et al (2010)
and Kolkman et al (2006)
, respectively and only up to 30% of isoenyzme families could be quantitatively resolved (Kolkman et al, 2006
; de Groot et al, 2007
; Gutteridge et al, 2010
). To score the comprehensiveness of our proteomics method, we used a model-based approach to assess which proteins can be expected to be present. On the basis of this analysis, the data reported here are 95–99% comprehensive. Our inability to detect up to 5% remaining proteins may be explained by other factors, such as (i) low abundance and lack of PTPs with good MS properties, (ii) occurrence of post-translational modifications that decrease or eliminate the signal of the corresponding target tryptic peptide or (iii) loss of the protein during the sample preparation steps (e.g., for cell wall or membrane proteins). The detectable proteins were consistently measured in unfractionated yeast proteome tryptic digests. This is in agreement with our earlier demonstration that proteins spanning the whole abundance range of the yeast proteome can be detected by SRM (Picotti et al, 2009
) and with the high degree of data reproducibility generated by the SRM technique demonstrated in a previous multi-center study (Addona et al, 2009
The applied method consistently quantified the target protein set across 15 samples, including biological triplicates, without the problem of missed data points. This number of consistently analyzed samples and replicates constitutes an improvement with respect to the lower reproducibility of data generated by shotgun proteomic studies. In fact, in many proteomic studies to date no replicate data sets were reported and only small numbers of samples were analyzed. This is in large part due to the significant effort and cost associated with generating comprehensive and quantitative proteomic data, especially where approaches based on in-depth fractionation were used. Also, the SRM approach allowed here for the quantitative discrimination of proteins with a high sequence overlap, such as isoenzymes, allowing us to gain insight into their functional diversification. Classical shotgun proteomic measurements based on automated peptide sequencing would be biased against the discrimination of isoenzymes, as their shared peptides are more likely to be high abundant and therefore preferentially detected. Overall, this study has resulted in the largest to date SRM-based proteomic data set (>200 proteins quantified across multiple conditions), with high comprehensiveness for the metabolic network under study, challenged with a set of nutritional conditions that imply radically different modes of metabolic operation. We expect that this will be a useful blueprint for further developing mathematical models of the yeast metabolism and a valuable basis for follow-up studies on the function of target (metabolic) proteins.
Despite their power in analyzing target proteins across several samples and replicates, SRM approaches are in their infancy and face still considerable technical challenges to their high-throughput application. The first is the need for designing optimal assays for each target protein. Recently, significant advances have been realized to speed up and automate this step and strategies based on unpurified synthetic peptides allow for the fast and low-cost development of SRM assays for essentially any protein or proteome of interest (Picotti et al, 2010
). Another challenge is the analysis of SRM data, which involves the detection and assignment of the relevant peaks in the raw MS data. Here, this step was carried out manually, using the most up to date and stringent confidence criteria (Anderson and Hunter, 2006
; Lange et al, 2008a
; Picotti et al, 2009
; MacLean et al, 2010
see details in the Materials and methods). However, manual peak assignment remains tedious and does not allow attributing a false discovery rate based on objective criteria to SRM-based peptide identifications. To this direction, algorithms are currently being developed to automate evaluation of SRM peak matches and their statistical treatment (Reiter L et al
, in preparation). The last bottleneck is the number of target proteins that can be concurrently analyzed in a single SRM run. This is at present significantly lower than that of proteins identified in a shotgun proteomics experiment on a high-performance MS (de Godoy et al, 2008
) and efforts are currently underway by the MS vendors to improve the multiplexing of this technology. For the specific network under study, the set of ~200 metabolic proteins can be quantified in ~4 h of instrument time per sample, measuring multiple SRM transitions per peptide, multiple peptides per protein (where available), with light (endogenous) and heavy (internal standard) signals. The data set presented here, consisting of 15 samples, can be acquired in ~3 days of mass spectrometric measurements.
Remarkably, the total number of metabolic proteins detectable, and thus expressed, did not change much between the different metabolic states. Although the identity of the necessary proteins varies slightly between conditions (Supplementary Table 3
), yeast cells should be able to grow with roughly 120 proteins in the considered network, yet many more are always present. Expression of unneeded proteins has long been known to reduce growth rates and thereby presumably evolutionary fitness (Dekel and Alon, 2005
); hence, intuition and genetic evidence (Zaslaver et al, 2004
) suggest that enzymes are downregulated under conditions when their reactions are not required. Their here demonstrated unexpected persistence might be explained by the lower than expected costs of unneeded protein synthesis after several generations of exponential growth, at least in Escherichia coli
(Shachrai et al, 2010
). Alternatively, it can be an adaptive strategy for rapid and flexible responses to environmental changes (Kotte et al, 2010
About 40% of the apparently ‘superfluous' proteins are isoenzymes. We showed here that differences in abundance changes among isoenzymes were indicative for different isoenzyme functionality, consistent with earlier studies based on transcriptional data (Ihmels et al, 2004
). On the basis of abundance pattern clustering in different functional classes of metabolic pathways, many isoenzymes show evidence for functional diversification in the presented experiments, which might explain their parallel presence in the S. cerevisiae
genome (Kuepfer et al, 2005
; Ihmels et al, 2007
). As we only tested a very limited number of conditions, it is our expectation that also for the isoenzymes that did not show functional diversification so far, such evidence could be found under appropriate metabolic setups.
In conclusion, this study shows that quantitative assays for large sets of biologically related proteins can be developed and deployed to monitor responses of these proteins to a set of different environmental or genetic conditions, providing detailed insights in a cell's physiology. This approach is ideal to explore the dynamics of cellular networks, under physiological or challenged conditions, also for organisms other than yeast, and thus has the potential to find broad applications in systems biology, biomedical and pharmaceutical research.