Functional genomic experiments frequently involve a comparison of the levels of gene expression between two or more genetic, developmental, or physiological states. Such comparisons can be carried out at either the RNA (transcriptome) or protein (proteome) level, but there is often a lack of congruence between parallel analyses using these two approaches. To fully interpret protein abundance data from proteomic experiments, it is necessary to understand the contributions made by the opposing processes of synthesis and degradation to the transition between the states compared. Thus, there is a need for reliable methods to determine the rates of turnover of individual proteins at amounts comparable to those obtained in proteomic experiments. Here, we show that stable isotope-labeled amino acids can be used to define the rate of breakdown of individual proteins by inspection of mass shifts in tryptic fragments. The approach has been applied to an analysis of abundant proteins in glucose-limited yeast cells grown in aerobic chemostat culture at steady state. The average rate of degradation of 50 proteins was 2.2%/h, although some proteins were turned over at imperceptible rates, and others had degradation rates of almost 10%/h. This range of values suggests that protein turnover is a significant missing dimension in proteomic experiments and needs to be considered when assessing protein abundance data and comparing it to the relative abundance of cognate mRNA species. Functional genomic experiments frequently involve a comparison of the levels of gene expression between two or more genetic, developmental, or physiological states. Such comparisons can be carried out at either the RNA (transcriptome) or protein (proteome) level, but there is often a lack of congruence between parallel analyses using these two approaches. To fully interpret protein abundance data from proteomic experiments, it is necessary to understand the contributions made by the opposing processes of synthesis and degradation to the transition between the states compared. Thus, there is a need for reliable methods to determine the rates of turnover of individual proteins at amounts comparable to those obtained in proteomic experiments. Here, we show that stable isotope-labeled amino acids can be used to define the rate of breakdown of individual proteins by inspection of mass shifts in tryptic fragments. The approach has been applied to an analysis of abundant proteins in glucose-limited yeast cells grown in aerobic chemostat culture at steady state. The average rate of degradation of 50 proteins was 2.2%/h, although some proteins were turned over at imperceptible rates, and others had degradation rates of almost 10%/h. This range of values suggests that protein turnover is a significant missing dimension in proteomic experiments and needs to be considered when assessing protein abundance data and comparing it to the relative abundance of cognate mRNA species. Four levels of analysis are commonly exploited in functional genomics: genome, transcriptome, proteome, and metabolome. The last three levels are all context-dependent; the complement of mRNA molecules, protein molecules, and metabolites all change with the physiological, developmental, or pathological state of living cells. A change in the proteome is probably the most important of these three for the analysis of gene action and interaction, but it is also the most difficult to study in a truly comprehensive manner (1.Miklos G.L. Maleszka R. Protein functions and biological contexts.Proteomics. 2001; 1: 169-178Google Scholar). "Classical" proteomics only compares amounts of proteins in cells in two different states or conditions; it does not address the dynamics of the proteome in the different biological states that are being compared nor does it provide information about the mechanisms whereby the system changes from one state to the other. The acquisition of a new steady-state level of any protein will be the outcome of the change in its rate of synthesis as compared with the change in its rate of degradation (2.Gottesman S. Maurizi M.R. Regulation by proteolysis: energy-dependent proteases and their targets.Microbiol. Rev. 1992; 56: 592-621Google Scholar, 3.Hochstrasser M. Johnson P.R. Arendt C.S. Amerik A. Swaminathan S. Swanson R. Li S.J. Laney J. Pals-Rylaarsdam R. Nowak J. Connerly P.L. The Saccharomyces cerevisiae ubiquitin-proteasome system.Philos. Trans. R. Soc. Lond. B Biol. Sci. 1999; 354: 1513-1522Google Scholar). At the steady state, it is the balance between these two opposing processes that determines the concentration of any protein (4.Benaroudj N. Tarcsa E. Cascio P. Goldberg A.L. The unfolding of substrates and ubiquitin-independent protein degradation by proteasomes.Biochimie (Paris). 2001; 83: 311-318Google Scholar). To illustrate, an increase in the level of expression of a protein could be achieved by an enhanced rate of synthesis or a diminished rate of degradation. Despite its evident importance, the role of protein turnover has not previously been considered in analyses of the proteome. Yet the determination of the half-life of a large number of proteome components might do much to explain the marked disparity that is sometimes seen between transcriptome and proteome data (5.Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol. Cell. Biol. 1999; 19: 1720-1730Google Scholar, 6.Ideker T. Thorsson V. Ranish J.A. Christmas R. Buhler J. Eng J.K. Bumgarner R. Goodlett D.R. Aebersold R. Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.Science. 2001; 292: 929-934Google Scholar, 7.Griffin T.J. Gygi S.P. Ideker T. Rist B. Eng J. Hood L. Aebersold R. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae..Mol. Cell. Proteomics. 2002; 1: 323-333Google Scholar, 8.Chen G. Gharib T.G. Huang C.C. Taylor J.M. Misek D.E. Kardia S.L. Giordano T.J. Iannettoni M.D. Orringer M.B. Hanash S.M. Beer D.G. Discordant protein and mRNA expression in lung adenocarcinomas.Mol. Cell. Proteomics. 2002; 1: 304-313Google Scholar). In addition, the requirements of one of the most energy-demanding processes in the cell, the aggregate process of protein synthesis and degradation, protein turnover, can be quantified on a protein-by-protein basis. In this article, we define an experimental strategy to analyze the dynamics of protein turnover, a missing dimension of proteomics. The diploid yeast strain BY4743 (EUROSCARF accession number Y23935, www.uni-frankfurt.de/fb15/mikro/euroscarf/index.html) (9.Brachmann C.B. Davies A. Cost G.J. Caputo E. Li J. Hieter P. Boeke J.D. Designer deletion strains derived from Saccharomyces cerevisiae S288c: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications.Yeast. 1998; 14: 115-132Google Scholar), a leucine auxotroph, was used throughout. Yeast were grown in glucose-limited chemostat culture as described previously (10.Baganz F. Hayes A. Farquhar R. Butler P.R. Gardner D.C. Oliver S.G. Quantitative analysis of yeast gene function using competition experiments in continuous culture.Yeast. 1998; 14: 1417-1427Google Scholar) in a medium (Table I) containing 100 mg/liter dl-[2H10]leucine (98.5 atom % excess) at a dilution rate of 0.1 h−1. After a minimum of seven doubling times, sufficient to ensure that cells were fully labeled, unlabeled l-leucine (1 g in 50 ml) was added, and the incoming medium was changed to one containing unlabeled l-leucine at 50 mg/liter. Sampling was at 0, 0.167, 0.667, 1, 2, 4, 6, 8, 10, 12, 24.5, and 51 h into the chase. This sampling frequency served to reduce the true dilution rate in the chase phase from a nominal 0.1 h−1 to an actual 0.086 h−1. At each time point, cells were collected directly into ice-cold tubes containing cycloheximide (final concentration 100 μg/ml). Cells (40 ml at an A600 of ∼1.6) were harvested and centrifuged at 5000 rpm for 5 min at 4 °C. The pellet was resuspended in 1 ml of ice-cold double distilled H2O and transferred to a 1.5-ml microcentrifuge tube. Cells were repelleted by centrifugation at 10,000 rpm, the supernatant was discarded, and the yeast pellet was frozen in dry ice and stored at −80 °C. Cell pellets were thawed briefly on ice and resuspended in 300 μl of 20 mm HEPES, pH 7.5 containing one EDTA-free protease inhibitor mixture tablet/10 ml (Roche Diagnostics) and lysed by vortexing with glass beads (6 × 45 s with 45 s of cooling). DNase (6 μl of l mg/ml, Sigma) and RNase (2 μl of l mg/ml, Sigma) were added, and the lysate was held at 4 °C for 1 h. The lysate was centrifuged at 4000 rpm for 10 min at 4 °C, and the supernatant was assayed for protein (Coomassie plus protein assay, Pierce).Table ICarbon-limited minimal medium for chemostat cultureComponentFinal concentrationg/literKH2PO42MgSO4·7H2O0.55NaCl0.1CaCl2·2H2O0.09Glucose2.5NH4SO43.13Uracil0.02Histidine0.02dl-Leucineadl-[2H10]Leucine was used in labeled media.0.1Trace elementsbTrace elements were made as two 10,000× stock solutions: solution 1, 10,000× FeCl3·6H2O; solution 2, 10,000× ZnSO4·7H2O, CuSO4·5H2O, H3BO, KI. ZnSO4·7H2OcVitamins were made as a 600× stock solution and stored at −20 °C.0.00007 CuSO4·5H2O0.00001 H3BO30.00001 KI0.00001 FeCl3·6H2O0.00005Vitamins Inositol0.062 Thiamine/HCl0.014 Pyridoxine0.004 Calcium pantothenate0.004 Biotin0.0003a dl-[2H10]Leucine was used in labeled media.b Trace elements were made as two 10,000× stock solutions: solution 1, 10,000× FeCl3·6H2O; solution 2, 10,000× ZnSO4·7H2O, CuSO4·5H2O, H3BO, KI.c Vitamins were made as a 600× stock solution and stored at −20 °C. Open table in a new tab Proteins (150 μg of soluble protein) from each of the 12 time points were then solubilized in 8 m urea, 2% (w/v) CHAPS, 20 mm dithiothreitol, and 0.5% 3–10 IPG Buffer (AP Biotech) for 1 h at 37 °C before centrifugation at 10,000 rpm for 10 min at 4 °C and application to 13-cm Immobiline pH 3–10 dry strips (AP Biotech) for in-gel rehydration (180 V-h at 30 V, 360 V-h at 60 V) and isoelectric focusing (500 V-h at 500 V, l000 V-h at 1000 V, and 16,000 V-h at 8000 V) using an IPGphor isoelectric focusing system (AP Biotech). Second-dimension analysis was by 12% (w/v) linear SDS-PAGE followed by Coomassie Blue staining. Gels were visually inspected, the same spot was excised from each gel, and peptides were obtained by in-gel reduction, alkylation with iodoacetamide, tryptic digestion, and extraction using a MassPrep™ digestion robot (Micromass, Manchester, UK). Peptides were analyzed using a MALDI-TOF mass spectrometer ([email protected]™, Micromass, Manchester UK) covering the m/z range of 1000–4000 Th. Spectra were stacked above each other, and the peak differences between 0 h (heavy, fully labeled) and 51 h (light, fully unlabeled) were identified. Intermediate time points showed the gradual disappearance of peptides carrying heavy leucine and the gradual appearance of peptides carrying unlabeled leucine. Depending on the number of leucine residues in the peptide, the "heavy" and "light" peptides differed in mass by 9n Da, where n was the number of leucine residues in the peptide. The protein was identified by recording the masses of peptides in the 51-h spectrum (fully unlabeled) and including the leucine composition of each peptide (derived from comparison of the 0- and 51-h spectra) in a manual search of the yeast data base using MASCOT (www.matrixscience.co.uk), which allows inclusion of composition data in its search (11.Pratt J.M. Robertson D.H. Gaskell S.J. Riba-Garcia I. Hubbard S.J. Sidhu K. Oliver S.G. Butler P. Hayes A. Petty J. Beynon R.J. Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.Proteomics. 2002; 2: 157-163Google Scholar). The monoisotopic peak intensities of the heavy and light tryptic peptides (AH and AL, respectively) were obtained and were used to calculate the relative isotope abundance at each time, t (RIAt), 1The abbreviations used are: RIA, relative isotope abundance; 2DGE, two-dimensional gel electrophoresis; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonic acid; MALDI-TOF, matrix-assisted laser desorption ionization-time of flight. as the ratio: RIAt= AH(AL+AH)(Eq. 1) The value of RIAt changes over time as the proteins, prelabeled with heavy leucine, are replaced by those labeled with light leucine. This is a consequence of two processes, namely loss of cells from the chemostat and loss through intracellular protein turnover. The generic form of the exponential equation relates the RIA at any time, t, to the values for RIA at t = 0 (RIA0) and t = ∞ (RIA∞, in practice, t = 51 h): RIAt=RIA∞+(RIA0−RIA∞) exp(−kloss×t)(Eq. 2) Rather than use non-linear curve fitting to recover the values of three parameters (RIA0, RIA∞, and kloss), RIA0 was measured for 26 peptides derived from a total of 18 different proteins and yielded a value of 0.985 ± 0.001 (mean ± S.E., n = 26). The variance in this experimentally determined parameter was so low that it was fixed as the mean value in the non-linear curve fitting. Similarly, the value for RIA∞ was set to zero since after 51 h, equivalent to seven doubling times, over 99% of the heavy labeled cells in the vessel at t = 0 h would have been lost from the vessel. By fixing these parameters, we also removed some of the error inherent in determination of the RIA at the start and end of the experiment where either the heavy or the light peak was small relative to the other and therefore the data were sometimes compromised by chemical noise in the mass spectrum. The fitted equation simplified to: RIAt=0.985 exp(−kloss×t)(Eq. 3) The curve of this form was fitted to the (t, RIAt) data using non-linear curve fitting to obtain kloss, the error in the parameter estimate and the confidence limits for the fitted curve. In single time point experiments, kloss was calculated from the value of RIAt determined at a single time, t, according to the equation: kloss=−ln(RIAt/0.985)/t(Eq. 4) Finally, the true rate of degradation (kdeg) was calculated by simple subtraction of the constant dilution rate D from kloss (the subtraction of a constant does not affect the error of the parameter estimate). Determination of the rate of turnover of specific proteins is fraught with difficulty, and our strategy was designed to yield turnover rates under carefully controlled conditions. Our approach measures the kinetics of labeling of proteins with stable isotope-labeled amino acids and uses mass spectrometry to determine the presence of those labeled amino acids in tryptic peptides. In this respect it differs from other studies that have prelabeled proteins with stable isotope-labeled amino acids either to compare expression levels (12.Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Google Scholar, 13.Jiang J. English A.M. Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine.J. Proteome Res. 2002; 1: 345-350Google Scholar), to determine the numbers of specific amino acids to aid in protein identification by peptide mass fingerprinting (11.Pratt J.M. Robertson D.H. Gaskell S.J. Riba-Garcia I. Hubbard S.J. Sidhu K. Oliver S.G. Butler P. Hayes A. Petty J. Beynon R.J. Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.Proteomics. 2002; 2: 157-163Google Scholar, 14.Hirayama K. Yuji R. Yamada N. Noguchi K. Yamaguchi Y. Enokizono J. Katao K. Arata Y. Shimada I. Convenient peptide mapping of immunoglobulin g2b and differentiation between leucine and isoleucine residues by mass spectrometry using 2h-labeled leucine.J. Mass. Spectrom. Soc. Jpn. 1998; 46: 83-89Google Scholar, 15.Chen X. Smith L.M. Bradbury E.M. Site-specific mass tagging with stable isotopes in proteins for accurate and efficient protein identification.Anal. Chem. 2000; 72: 1134-1143Google Scholar, 16.Hunter T.C. Yang L. Zhu H. Majidi V. Bradbury E.M. Chen X. Peptide mass mapping constrained with stable isotope-tagged peptides for identification of protein mixtures.Anal. Chem. 2001; 73: 4891-4902Google Scholar, 17.Engen J.R. Bradbury E.M. Chen X. Using stable-isotope-labeled proteins for hydrogen exchange studies in complex mixtures.Anal. Chem. 2002; 74: 1680-1686Google Scholar), or to determine post-translational modifications (18.Zhu H. Hunter T.C. Pan S. Yau P.M. Bradbury E.M. Chen X. Residue-specific mass signatures for the efficient detection of protein modifications by mass spectrometry.Anal. Chem. 2002; 74: 1687-1694Google Scholar). We measured the rate of turnover of several Saccharomyces cerevisiae proteins in glucose-limited aerobic continuous culture at the steady state. In continuous culture, the cells are maintained in a constant metabolic state, and the gain in biomass through growth is balanced by physical loss of cells as the cell suspension is displaced by incoming fresh medium. In this respect, continuous culture is superior to growth in "batch" culture in which nutrients are depleted, cell number increases, medium pH can fall, and the rate of growth declines. The yeast cells were grown at a fixed dilution (doubling) rate, and proteins were uniformly labeled with a deuterated amino acid provided in the incoming growth medium. Subsequently a large excess of unlabeled amino acid was added instantaneously to the culture medium, and at the same time, the medium reservoir was switched to one containing the unlabeled amino acid. Because the cells are glucose-limited, the addition of an excess of the unlabeled amino acid does not affect the growth rate, but the labeled proteins are degraded and/or diluted into the daughter cells. The chosen precursor was decadeuterated leucine, labeled at all positions other than the α-amino and α-carboxyl groups and chosen because leucine is present in the great majority of tryptic peptides derived from the yeast proteome (11.Pratt J.M. Robertson D.H. Gaskell S.J. Riba-Garcia I. Hubbard S.J. Sidhu K. Oliver S.G. Butler P. Hayes A. Petty J. Beynon R.J. Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.Proteomics. 2002; 2: 157-163Google Scholar). Use of a leucine auxotrophic mutant of S. cerevisiae ensured that dilution of the label by endogenous leucine would be minimized. Finally, we have shown that the deuterium atom bonded to the α-carbon atom is metabolically labile (probably through transamination), and the relative incorporation of decadeuterated or nonadeuterated leucine provides a valuable insight into the metabolic lability of the precursor pool, important information in establishing the effectiveness of the labeling strategy (11.Pratt J.M. Robertson D.H. Gaskell S.J. Riba-Garcia I. Hubbard S.J. Sidhu K. Oliver S.G. Butler P. Hayes A. Petty J. Beynon R.J. Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.Proteomics. 2002; 2: 157-163Google Scholar, 14.Hirayama K. Yuji R. Yamada N. Noguchi K. Yamaguchi Y. Enokizono J. Katao K. Arata Y. Shimada I. Convenient peptide mapping of immunoglobulin g2b and differentiation between leucine and isoleucine residues by mass spectrometry using 2h-labeled leucine.J. Mass. Spectrom. Soc. Jpn. 1998; 46: 83-89Google Scholar). Proteins were prelabeled with heavy leucine for approximately 50 h, more than seven doubling times at a dilution rate of 0.1 h−1. Thus, over 99% of the leucine in the cells would be the stable isotope-labeled form. Replacement of the entire leucine pool in this way had no effect on growth rate or on the overall pattern of proteins in a two-dimensional gel (results not shown). The chemostat was then injected with a large excess (20-fold) of unlabeled l-leucine to rapidly reduce the isotope abundance of the precursor pool. The feedstock was switched to unlabeled leucine, and samples of cells were taken from the culture vessel over the next 51 h. The unlabeled leucine pulse added to the growth vessel had no effect on CO2 production, demonstrating that leucine was not being used as an alternative carbon source. Further, there were no quantitative differences in 2DGE patterns from cells before or after the chase with unlabeled leucine (results not shown). During the chase period, all newly synthesized proteins would only incorporate unlabeled leucine. The rate of loss of labeled protein (kloss) is a composite term reflecting the sum of losses through dilution of the cells (synthesis de novo) or through degradation (Fig. 1). At each sampling time before and throughout the chase period, cells were lysed, and proteins from the cleared lysate were separated by 2DGE. The pattern of spots on the gels was very consistent (although this is not a prerequisite of the approach), and we could recover the same protein from each gel, which was then subjected to in-gel tryptic digestion followed by MALDI-TOF mass spectrometry (19.Shevchenko A. Jensen O.N. Podtelejnikov A.V. Sagliocco F. Wilm M. Vorm O. Mortensen P. Boucherie H. Mann M. Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels.Proc. Natl. Acad. Sci. U. S. A. 1996; 93: 14440-14445Google Scholar). The profiles for individual peaks in the trypsin peptide mass fingerprint tracked the replacement of the labeled protein by the unlabeled protein as the cells continued to grow in culture. A representative set of MALDI-TOF data over 51 h, expanded to emphasize the behavior of individual peptides with one or multiple leucine residues, shows that the transition from fully labeled to fully unlabeled peptides was readily apparent (Fig. 2). Proteins were identified by peptide mass fingerprinting supplemented by the data on the leucine composition of each peptide derived from the separation between the heavy and light peaks (see "Experimental Procedures"). Incidentally, if the precursor pool had not been effectively "chased," peptides of intermediate masses, distributed binomially, would be expected (20.Papageorgopoulos C. Caldwell K. Shackleton C. Schweingrubber H. Hellerstein M.K. Measuring protein synthesis by mass isotopomer distribution analysis (MIDA).Anal. Biochem. 1999; 267: 1-16Google Scholar). However, for peptides containing more than one leucine residue, the lack of peaks of mass values intermediate between the fully labeled and fully unlabeled forms is convincing proof that the relative isotope abundance of the precursor pool had been efficiently reduced to zero, a prerequisite of this approach. Of course, the lower limit on detection of these peptides of intermediate mass is influenced by the background noise in the spectrum. However, the lack of any such peaks above the noise floor is good evidence for an effective chase. The natural isotope abundance profiles of labeled peptides also confirmed that virtually all of the [2H10]leucine supplied in the medium was converted to [2H9]leucine in vivo (probably through transamination). The intensity of the monoisotopic peaks of the heavy and light tryptic fragments were measured for each sampling time point in the chase phase. The transition in intensity between the fully labeled and the fully unlabeled leucine-containing peptides is most simply defined by a single exponential curve that yields the first order rate constant for loss of the label from the protein (kloss). To determine this rate constant, a single exponential curve was fitted to the set of RIAt data for each leucine-containing tryptic peptide. Since multiple peptides in a single peptide mass fingerprint would be expected to contain leucine, each peptide should deliver an independent measure of the rate of turnover of the parent protein. For each protein, the first order rate constant was remarkably consistent whether derived from peptides with one or more than one leucine residue (Fig. 3). The errors in the fitted rate constants were typically less than 10% of the parameter value, and the concordance between the degradation rates, defined by multiple peptides derived from a single protein, was high. For all further analyses, we pooled the data from different peptides to yield a single fitted curve based on multiple determinations of RIAt at each time point (Table II, spots 1–31).Table IIRates of degradation of yeast proteinsSpot no.Protein IDPeptide (observed [M + H]+) [number of Leu residues]Isotope removal (kloss) (h−1), curve fitting analysis (kloss ± S.E. (n))Isotope removal (kloss), single point determination (mean kloss ± S.E. (n))1Heat shock protein SSA1 (HS71)1552.98 [2]0.0972 ± 0.0028 (12)2130.22 [2]0.1035 ± 0.0040 (12)1199.94 [2]0.1117 ± 0.0081 (12)1431.01 [1]0.1012 ± 0.0078 (12)All peptides0.1032 ± 0.0051 (48)2Pyruvate decarboxylase1997.98 [1]0.0901 ± 0.0021 (10)1597.01 [1]0.0930 ± 0.0029 (10)1689.90 [1]0.0897 ± 0.0035 (10)All peptides0.0919 ± 0.0024 (30)0.0976 ± 0.0062 (10)3NADP-specific glutamate dehydrogenase1188.80 [1]0.0717 ± 0.0024 (10)1605.14 [1]0.0688 ± 0.0059 (10)1468.86 [1]0.0721 ± 0.0041 (10)All peptides0.0709 ± 0.0025 (30)0.0633 ± 0.0024 (6)4Heat shock protein SSA1 or -2 (HS71/72) (fragment)1552.98 [1]0.1033 ± 0.0034 (12)1675.90 [1]0.1100 ± 0.0076 (12)1431.03 [1]0.1074 ± 0.0057 (12)1816.07 [2]0.1167 ± 0.0083 (11)All peptides0.1092 ± 0.0049 (47)0.1221 ± 0.0033 (10)5Enolase II1876.14 [1]0.0865 ± 0.0038 (12)1431.03 [2]0.0906 ± 0.0069 (12)2741.17 [2]0.0909 ± 0.0023 (12)All peptides0.0893 ± 0.0041 (36)0.0851 ± 0.0019 (10)6Enolase I pI ∼7.51876.11 [1]0.0933 ± 0.0027 (12)1856.98 [1]0.0993 ± 0.0103 (12)2441.11 [2]0.1048 ± 0.0075 (11)All peptides0.0950 ± 0.0058 (35)7Enolase I pI ∼7.71822.08 [2]0.1130 ± 0.0077 (11)2441.47 [2]0.0987 ± 0.0024 (11)1578.98 [2]0.1190 ± 0.0128 (11)1373.87 [1]0.0998 ± 0.0025 (11)All peptides0.1034 ± 0.0029 (44)0.0952 ± 0.0036 (10)8Phosphoglycerate kinase1440.00 [3]0.1145 ± 0.0025 (11)1768.14 [3]0.1079 ± 0.0026 (11)1668.04 [2]0.1058 ± 0.0025 (11)2327.27 [1]0.1005 ± 0.0042 (11)2039.14 [2]0.1005 ± 0.0051 (11)All peptides0.1058 ± 0.0027 (55)0.1085 ± 0.0045 (10)9Yol 154wp1617.94 [2]0.1273 ± 0.0050 (12)1961.58 [1]0.1155 ± 0.0111 (11)1228.75 [1]0.1202 ± 0.0027 (10)All peptides0.1224 ± 0.0048 (33)0.1241 ± 0.0026 (8)10Fructose-bisphosphate aldolase2160.21 [1]0.1209 ± 0.0073 (12)1794.99 [1]0.1157 ± 0.0053 (12)1863.04 [1]0.1273 ± 0.0069 (12)2035.05 [1]0.1178 ± 0.0039 (12)All peptides0.1203 ± 0.0054 (48)0.1270 ± 0.0035 (8)11Ketol-acid isomerase1355.99 [1]0.0760 ± 0.0019 (11)1170.06 [1]0.0790 ± 0.0039 (12)1181.94 [1]0.0834 ± 0.0068 (12)1306.09 [1]0.0781 ± 0.0024 (11)1530.13 [1]0.0772 ± 0.0020 (11)1753.27 [2]0.0857 ± 0.0041 (11)2099.52 [3]0.0902 ± 0.0068 (11)2145.45 [1]0.0801 ± 0.0057 (11)All peptides0.0805 ± 0.0033 (90)0.0680 ± 0.0021 (14)12Enolase I fragment1159.82 [1]0.1091 ± 0.0174 (9)1856.97 [1]0.1029 ± 0.0036 (10)1578.91 [2]0.1149 ± 0.0128 (9)1373.78 [1]0.1053 ± 0.0077 (9)1756.03 [1]0.1084 ± 0.0056 (9)All peptides0.1075 ± 0.0042 (37)13Phosphoglycerate kinase fragment1440.01 [3]0.1113 ± 0.0035 (9)1668.06 [2]0.1034 ± 0.0037 (9)All peptides0.1073 ± 0.0034 (18)14Glyceraldehyde-3-phosphate dehydrogenase 31752.78 [1]0.0891 ± 0.0021 (8)2591.20 [2]0.0938 ± 0.0078 (9)All peptides0.0908 ± 0.0045 (17)15Enolase I fragment1856.96 [1]0.1003 ± 0.0063 (12)1578.89 [2]0.1164 ± 0.0150 (11)1373.78 [1]0.1061 ± 0.0112 (11)All peptides0.1071 ± 0.0100 (34)15Glyceraldehyde-3-phosphatase dehydrogenase 21752.87 [1]0.0987 ± 0.0028 (12)16Malate dehydrogenase1395.92 [1]0.1108 ± 0.0030 (11)1350.03 [2]0.1227 ± 0.0039 (11)1592.96 [1]0.0923 ± 0.0086 (10)1332.90 [1]0.1115 ± 0.0058 (10)1638.01 [1]0.1062 ± 0.0086 (11)All peptides0.1087 ± 0.0043 (53)0.1077 ± 0.0166 (8)17Glyceraldehyde-3-phosphate dehydrogenase 31749.49 [1]0.0945 ± 0.0017 (7)18Glyceraldehyde-3-phosphate dehydrogenase 31750.67 [1]0.0933 ± 0.0013 (9)19Heat shock protein 261806.21 [1]0.1262 ± 0.0150 (7)2040.57 [2]0.1265 ± 0.0228 (7)All peptides0.1263 ± 0.0187 (14)0.1260 ± 0.0083 (6)20Fructose-bisphosphate aldolase2390.20 [1]0.0998 ± 0.0085 (6)1863.07 [1]0.0958 ± 0.0066 (6)2035.13 [1]0.0936 ± 0.0024 (6)All peptides0.0964 ± 0.0044 (18)21Adenylate kinase1456.01 [2]0.1146 ± 0.0021 (7)1994.30 [2]0.1166 ± 0.0080 (8)All peptides0.1152 ± 0.0055 (15)0.1299 ± 0.0139 (10)22Triosephosphate isomerase1096.83 [1]0.1019 ± 0.0017 (12)1252.86 [1]0.0932 ± 0.0042 (12)2763.38 [2]0.0954 ± 0.0038 (8)All peptides0.0966 ± 0.0019 (32)0.1035 ± 0.0039 (10)23Glyceraldehyde-3-phosphate dehydrogenase 3 fragment1753.06 [1]0.0928 ± 0.0038 (10)0.1046 ± 0.0066 (3)25Glyceraldehyde-3-phosphate dehydrogenase 3 fragment1820.08 [2]0.0991 ± 0.0043 (10)26No ID1752.47 [1]0.1055 ± 0.0084 (6)27Glyceraldehyde-3-phosphate dehydrogenase 2 fragment1752.94 [1]0.1046 ± 0.0067 (11)27No ID1785.86 [2]0.1098 ± 0.0090 (5)28No ID1737.86 [2]0.1130 ± 0.0550 (5)29Cpn101591.96 [1]0.1087 ± 0.0078 (7)1525.93 [2]0.1112 ± 0.0078 (7)All peptides0.0978 ± 0.0110 (14)30Peptidylprolyl cis-trans isomerase2098.05 [1]0.1155 ± 0.0112 (7)0.089 (1)31Heat shock protein 121437.80 [1]0.1126 ± 0.0076 (7)1745.84 [1]0.1094 ± 0.0092 (8)All peptides0.1111 ± 0.0080 (15)0.1037 ± 0.0222 (4)Spot no.Protein IDPeptide (observed [M + H]+)Degradation rate (h−1), single point (4 h, 8 h)Degradation rate (h−1), single point determination, all peptides (mean ± S.E. (n))33Microsomal protein of CDC48/PAS1/SEC18 family of ATPases1756.11 [3]0.1612, 0.14621246.84 [2]0.1276, 0.14571578.04 [2]0.1274, 0.1229All peptides0.1385 ± 0.0061 (6)34Actin binding protein; Abp1p1583.22 [1]0.0859, 0.09212133.39 [1]0.0730, 0.09072662.76 [1]0.0719, 0.09361715.25 [1]0.1161, 0.10743035.64 [1]0.0855, 0.1027All peptides0.0902 ± 0.0035 (10)35Kar 2p1199.88 [2]0.0773, 0.081871197.87 [1]0.0985, 0.08051316
