All living organisms rely on the uptake of nutrients from the environment to sustain energy, metabolism and growth. They have therefore evolved numerous alternative programs to adapt to their permanently changing environment. Such programs involve instantaneous responses (changes in intracellular metabolites, activation/inhibition of enzymes by effectors and of proteins through post-translational modifications) as well as slower processes that affect the levels of macromolecules (transcription, translation, mRNA and protein degradation). The availability of complete genome sequences and technologies that allow comprehensive analysis of global mRNA profiles has greatly expanded the ability to monitor the transcriptional reprogramming of cells in response to their environment. However, further studies (often conducted with yeast) indicate that transcripts are imperfect indicators of protein levels and of in vivo
fluxes (Griffin et al, 2002
; Washburn et al, 2003
; Daran-Lapujade et al, 2004
), and therefore bring limited understanding on whole biological systems.
Other ‘omics' tools are not developed to the same degree as transcriptomics. Especially, quantitative analysis of the complete proteome still remains a major challenge. Conventional quantitative proteome analysis utilizes two-dimensional (2D) gel electrophoresis (O'Farrell, 1975
) to separate complex protein mixtures followed by in-gel tryptic digestion and mass spectrometry for the identification of proteins. Although 2D gel electrophoresis allows the separation of hundreds of proteins simultaneously, it suffers many well-documented drawbacks such as the poor gel-to-gel reproducibility, the under-representation of low-abundant and hydrophobic proteins and the poor dynamic range of detection (Fey and Larsen, 2001
; Rabilloud, 2002
). To overcome some of the shortcomings of 2D gel electrophoresis, alternatives have been developed for quantitative proteomics. One of the most promising approaches relies on the labeling of proteins with stable isotopes (for reviews see Romijn et al, 2003
; Julka and Regnier, 2004
). The isotopic label can be incorporated into proteins via metabolic labeling of the living cells or into protein/peptides via chemical reaction after protein extraction. Mixed labeled and unlabeled protein extracts are then separated and analyzed by mass spectrometry and the relative abundance of proteins can be determined by comparison of the integrated mass spectrometry peak areas of the labeled and unlabeled forms of the peptides. Metabolic labeling offers the earliest time point of stable isotope incorporation and is thus one of the most comprehensive labeling methods. Cells are grown on normal-abundance or stable-isotope-labeled media and the label is incorporated during protein synthesis. So far, different isotopic labels have been used, for example, 15
N fully labeled media (Oda et al, 1999
; Krijgsveld et al, 2003
), or 13
C- or 2
H- labeled amino acids (Ong et al, 2002
; Blagoev et al, 2003
), and metabolic labeling has been applied to a wide variety of organisms ranging from bacteria (Conrads et al, 2001
), fruitflies (Krijgsveld et al, 2003
) to rats (Wu et al, 2004a
In quantitative approaches, not only the analytical techniques but also the experimental designs for cell cultivation have to be thoroughly devised. Laboratory-scale cultivation of microorganisms is predominantly performed in shake flasks. During the course of these batch fermentations, the physical and chemical environment constantly changes, which affects the specific growth rate and the regulation of many metabolic processes. As the time constants of transcription and translation are likely to differ, this dynamic nature of batch cultures complicates studies on correlation between mRNA and protein levels. The use of chemostat cultures enables the study of physiological adaptations to steady-state nutrient-limited growth. The medium that is continuously fed into a chemostat can be designed such that growth is limited by a single, defined nutrient, whereas all other nutrients remain present in excess. The culture broth is continuously replaced by fresh medium at a fixed and accurately determined dilution rate. This results in a constant dilution rate, which is equal to the growth rate. This offers the unique possibility to study metabolism and its regulation at a fixed and constant growth rate under tightly defined nutritional conditions, making chemostats excellent tools for quantitative transcriptome and proteome studies (Daran-Lapujade et al, 2004
; Kolkman et al, 2005
; Tai et al, 2005
). Furthermore, the use of fermenters instead of shake flasks enables the tight control of critical culture parameters (e.g. pH, aeration, temperature).
In their natural environments, as well as in industrial processes, growth of microorganisms is often limited by a single nutrient. For instance, during baker's yeast production, yeast is grown aerobically under sugar limitation to achieve high biomass yields. Conversely, during processes such as beer fermentation and dough fermentation, high concentrations of fermentable sugars are present under anaerobic conditions, and growth is limited by other nutrients (e.g. oxygen and/or nitrogen). In recent large-scale transcriptome analyses of Saccharomyces cerevisiae
grown under various nutrient limitations (Boer et al, 2003
; Daran-Lapujade et al, 2004
; Saldanha et al, 2004
; Wu et al, 2004b
; Tai et al, 2005
), specific transcriptional responses to the limiting nutrient were identified. However, it is not known to what extent these major transcriptional responses are actually translated into quantitatively identical responses at the protein level.
The aim of this study was to investigate the proteomic response of S. cerevisiae grown under different nutrient limitation regimes (carbon and nitrogen limitation) and to assess to what extent changes at the transcriptional level are reflected in changes at the protein level. For this purpose, we performed a quantitative proteome analysis of chemostat-cultivated S. cerevisiae limited for glucose or ammonia. The proteins were labeled in vivo by metabolic stable isotopic labeling with 15N and quantified by mass spectrometry. The protein data set was less biased compared to standard 2D gel-based analysis, as proteins originating from different subcellular compartments, including membrane proteins, and proteins with extreme isoelectric points and molecular weights were identified. In order to reduce error and noise in the data, we used rather stringent criteria to filter the protein expression levels, resulting in a data set of 102 proteins that were considered as significantly changed. The functional annotation of these 102 proteins provided insight into how the yeast cell copes with nitrogen and carbon limitation at the protein level. Moreover, by comparing the proteome data with corresponding transcriptome data, it was found that transcriptional control mechanisms play a significant role in gene expression regulation under glucose limitation, whereas under ammonia limitation protein expression was mainly regulated post-transcriptionally. These observations clearly underline the need for multilevel analysis in yeast systems biology approaches.