Perturbation and Interpretation of Nitrogen Isotope Distribution

Nov 18, 2005 - Proteome Turnover in the Green Alga Ostreococcus tauri by Time CourseN Metabolic ... Journal of Proteome Research 2007 6 (1), 97-104...
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Perturbation and Interpretation of Nitrogen Isotope Distribution Patterns in Proteomics Ambrosius P. L. Snijders, Bart de Koning, and Phillip C. Wright* Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom Received August 9, 2005

This study provides a discussion on the applications and limitations of 15NH4+ metabolic labeling in proteomic studies. The hyperthemophilic crenarchaeon Sulfolobus solfataricus was used as a model organism throughout this study. The distribution of nitrogen was studied in four different experiments in which this distribution was manipulated in a unique way. The experiments included full adaptation to media with relative isotope abundances (RIA) of 0.36%, 50%, and >98% 15NH4+. The incorporation efficiency was calculated on the basis of a comparison between theoretical and experimental spectra. In the case of full adaptation, incorporation efficiencies reflected the RIA (0.36%, 47.5% and 99% respectively). Labeling efficiencies were calculated on the basis of peak areas in TOF-MS spectra. It is shown that in the case of full adaptation, labeling efficiencies are 100%. In addition, we demonstrate that 15NH4+ labeling can be used in protein turnover studies, even when labeling is incomplete. In this case, incorporation efficiencies of 88-93% (lower than the RIA) were measured, providing evidence for amino acid recycling. Labeling efficiencies were always between 63% and 94% providing evidence for protein degradation. Finally, it was shown that isotope distributions can be useful in peptide identification. Keywords: metabolic labeling • protein turnover • proteomics • archaea • Sulfolobus solfataricus

Introduction Stable isotope labeling of proteins and peptides has become a popular tool in proteomics. With this technique, the relative abundances of labeled and unlabeled peptides corresponding to two different protein samples can be measured by mass spectrometry.1,2 Labeling can be achieved by: (I) chemical (e.g., ICAT), (II) enzymatic (e.g., 18O labeling with trypsin) or (III) metabolic incorporation of stable isotopes.3-7 In metabolic labeling, a heavy isotope containing nutrient is supplied to the growth medium. Heavy and light isotopes have the same physicochemical properties, and therefore, the cell does not make a distinction between them.8 During growth, the nutrient is assimilated and incorporated into cell material as usual. The difference between the heavy and light version of the peptide only becomes apparent in the MS spectrum.9 Popular approaches for metabolic labeling include stable isotope labeling with amino acids in cell culture (SILAC) and 15 NH4+ labeling.10,11 Metabolic labeling strategies provide a number of advantages over chemical and enzymatic labeling. These are as follows: (I) short-term exposure of the organism to stable isotope labeled precursors allows for protein turnover studies12 and (II) incorporation of stable isotope precursors can provide valuable sequence information.13-16 Protein turnover studies are facilitated by the availability of auxotrophic strains or mutants.17 Unfortunately, the genetic tools to generate auxotrophic mutants are only available for a limited number of organisms. Moreover, the cells’ metabolism and proteome 10.1021/pr050260l CCC: $30.25

 2005 American Chemical Society

will be affected by the mutation. The use of 15NH4+ labeling can avoid these problems. However, 15NH4+ labeling in protein turnover studies is sometimes considered problematic because of incomplete labeling of newly synthesized proteins.7,18 Despite this perception, we show the feasibility of protein turnover studies based on 15NH4+ labeling in the hyperthermophilic crenarchaeon S. solfataricus. This organism grows optimally at a temperature of 80 °C, and little is known about protein turnover at these extreme conditions. In a number of experiments, the relative abundance of isotopes (RIA) in the medium was manipulated, and the effects on the isotope distribution patterns of cell material were investigated. The distribution of isotopes in proteins and cell material in general is ultimately dependent on the metabolic pathways involved in nitrogen metabolism. For protein synthesis, the cell can use two different nitrogen (amino acid) pools, obtained from two different processes: (I) Ammonium assimilation from the medium. Ammonium in the case of S. solfataricus is transferred to 2-oxoglutarate through the sequential action of glutamine synthase and glutamate synthase.19 Subsequently, transaminases are used to build the remaining amino acids from precursors provided by the TCA cycle and glycolysis. Next, amino acids are coupled to t-RNA and converted into proteins by the ribosome according to the instructions encoded in the mRNA. (II) Amino acid recycling as a result of protein turnover. When these proteins are degraded by proteases and converted Journal of Proteome Research 2005, 4, 2185-2191

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into individual amino acids, the nitrogen atoms become part of the amino acid pool from which new proteins can be synthesized. In this study, experiments were designed to examine the relative contributions of both of these mechanisms.

Materials and Methods Cell Growth and Metabolic Labeling. S. solfataricus cultures were grown aerobically at a temperature of 80°C and a pH of 4.0. Each flask contained 50 mL basic medium consisting of 2.5 g/L (NH4)2SO4, 3.1 g/L KH2PO4, 203.3 mg/L MgCl2 • 6 H2O, 70.8 mg/L Ca(NO3)2 • 4 H2O, 2 mg/L FeSO4 • 7 H2O, 1.8 mg/L MnCl2 • 4 H2O, 4.5 mg/L Na2B4O7 • 2 H2O, 0.22 mg/L ZnSO4 • 7 H2O, 0.06 mg/L CuCl2 • 2 H2O, 0.03 mg/L Na2MoO4 • 2 H2O, 0.03 mg/L VOSO4 • 2 H2O, 0.01 mg/L CoCl2 • 6 H2O, 25 µL Wolfe’s vitamins and the carbon source (D-glucose or Darabinose) in a final concentration of 0.3-0.4% (w/v).20 In the case of the 15N labeling experiments the same medium was used, but the nitrogen source was replaced by (15NH4)2SO4 (Sigma-Aldrich). To ensure full incorporation of the isotope label into all proteins, at least 8 doubling times were allowed (approximately 48 h). Protein Extraction and SDS-PAGE. Cells were pelleted by centrifugation for 10 min at 16 060 × g. Next, cell pellets were redissolved in Laemli buffer (Bio-Rad, Hercules, CA) and subsequently heated at 100 °C for 15 min to ensure cell lysis. Subsequently, insoluble material was removed by centrifugation for 10 min at 16 060 × g. The supernatant was then loaded on a 10% T (% acrylamide + % N,N-methylenbisacrylamide), 2.6% C (% N,N-methylenbisacrylamide.100/T) SDS-PAGE gel (7 cm × 7 cm × 0.75 mm). Electrophoresis was carried out for 40 min at 200 V using a mini Protean III electrophoresis cell (Bio-Rad, Hercules, CA). Gels were stained with Coomassie brilliant blue R250 (Sigma-Aldrich). Tryptic Digestion and Peptide Extraction. Gel bands of interest were manually excised with a scalpel and transferred to 1.5 mL Eppendorf tubes. Then, they were destained with 200 mM ammonium bicarbonate with 40% acetonitrile for 30 min at 37°C (2×). The gel pieces were incubated overnight in trypsin solution (0.4 µg trypsin (Sigma-Aldrich) and 50 µL of 40 mM ammonium bicarbonate in 9% acetonitrile). Subsequently, peptides were extracted in three subsequent extraction steps using 5 µL of 25 mM ammonium bicarbonate (10 min, room temperature), 30 µL acetonitrile (15 min, 37 °C), 50 µL of 5% formic acid (15 min, 37 °C) and finally with 30 µL acetonitrile (15 min, 37 °C). Extracts were then pooled and dried down to completeness in a vacuum centrifuge. Peptide Identification. Dried extracts were redissolved in 3% acetonitrile and 0.1% Formic acid. Peptides were then loaded onto a PepMap C-18 RP capillary column (LC Packings, Amsterdam, The Netherlands), and eluted in a 30-minute gradient via a LC Packings Ultimate nanoLC directly onto a QStarXL electrospray ionization quadrupole time-of-flight tandem mass spectrometer (ESI qQ-TOF; Applied Biosystems/ MDS Sciex). Data acquisition on the MS was performed in the positive ion mode using Information Dependent Acquisition (IDA). Peptides with charge states 2+ and 3+ were selected for fragmentation. IDA data were submitted to Mascot (version 1.6b10) for database searching. The settings were as follows: peptide tolerance 1.0 Da; MS/MS tolerance 0.6 Da; carbamidomethyl modification of cysteine was set as a fixed modification; methionine oxidation was set as a variable modification: maximal 1 missed cleavage site by trypsin was allowed. The 2186

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Figure 1. Experimental design. td ) doubling time.

search was performed against the Mass Spectrometry protein sequence DataBase (MSDB). MOWSE scores greater than 50 were considered as significant. Experimental Design. Four different experiments were carried out. A summary of the experimental design is presented in Figure 1, with the full details described in the following paragraphs. Experiment 1. Two independent cell cultures were set up. Cells were adapted for 8 doubling times to medium with RIA values of 0.36% (natural abundance) and >98% 15NH4+. Next, the cultures were mixed in a 1:1 ratio, on the basis of OD530 measurements as described previously.15 This experiment was performed to create a sample in which 50% of the biomass contained 14N and 50% contained 15N. Experiment 2. Cells were adapted for 8 doubling times to a medium with a RIA value of 50% 15NH4+. In this experiment, 50% of the biomass contains 14N and 50% contains 15N. However, this was achieved in a fundamentally different way compared to experiment 1. Experiment 3. Cells were initially grown in the presence of 14 NH4 (RIA ) 0.36%). Halfway through the experiment, cells were pelleted by centrifugation at 16 060 × g and washed twice with 15NH4+ containing medium (RIA > 98%). After this, the cells were allowed to grow for one doubling time in the 15NH4+ containing medium and then they were harvested. In this way, 50% of the biomass was created in the presence of 14NH4+ and 50% in the presence of 15NH4+. For both these media glucose served as the carbon source. Experiment 4. The setup of this experiment was similar to experiment 3. However, in this case glucose was the carbon source during the initial growth and arabinose was the carbon source after the medium replacement. Determination of the Labeling Efficiency. Peptide areas were calculated using the LC-MS reconstruction tool within the Analyst Qs software package service pack 8 (Applied Biosystems). Determination of the Incorporation Efficiency. Theoretical mass spectra were calculated using IsoPro 3.0 software (http:// members.aol.com/msmssoft/) based on the Yergey algorithm.21 The mean square error (MSE) (also called the sum of the square of the deviations) method was used to calculate incorporation efficiencies. For each mass, Mi, in the isotope distribution, the difference in theoretical and experimental abundance, ∆Ai was calculated. Next, the sum of the squares of this difference

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Figure 2. (A) TOF-MS spectrum of the example peptide FIAEGAELNK obtained from experiment 1. (B) MSE plot of labeled and unlabeled peptides from experiments 1. The insets in Figure 2A represent theoretical spectra obtained with Isopro 3.0 based on the incorporation efficiency calculated from the MSE plot (0.36% and 99.5% for the unlabeled and labeled peptides respectively). Insets are provided to illustrate the similarity between theoretical and experimental spectra.

∑(∆Ai)2 was calculated, representing the error. This was done for a range of theoretical spectra. The lower the value of this error, the higher is the similarity between theoretical and experimental spectra. The incorporation efficiency was then determined as the percentage of incorporation, where the error ∑ (∆Ai)2 was minimal.

Results and Discussion In this part of the paper, TOF-MS spectra of an example peptide throughout the 4 experiments are discussed. The amino acid sequence of this peptide was FIAEGAELNK and corresponds to the thermosome subunit A (ThsA) from S. solfataricus. The elemental composition of this peptide is C49H79N12O16 and the intact mass is 1090.57 Da. Labeling and Incorporation Efficiency. Figure 2A shows the TOF-MS spectrum of the example peptide obtained from experiment 1 (mixture of 14NH4+ adapted and 15NH4+ adapted cells). In this case, two distinct series of peaks are present in the spectrum. The first (at m/z ) 546.3) corresponds to the monoisotopic peak of the unlabeled (14N) version of the peptide. Because the peptide contains 12 nitrogen atoms, the mass difference between labeled and unlabeled peptides should be 12 Da. Since the peptide charge is 2+, the 15N monoisotopic can be found at m/z ) 552.3. The sample created in experiment 1 contains two different protein populations (unlabeled and labeled). Therefore, it can be considered heterogeneous.

Figure 3. (A) TOF-MS spectrum and (B) MSE plot of the example peptide FIAEGAELNK obtained from experiment 2. Insets in Figure 3A show the theoretical spectra of the unlabeled (0.36%) and the theoretical spectrum corresponding to an incorporation efficiency of 47.5%. The theoretical spectrum of the unlabeled peptide is provided to show that the labeling efficiency ≈ 100%.

Figure 3A shows the TOF-MS spectrum of the example peptide obtained from experiment 2 (full adaptation to a medium with a RIA of 50%). In contrast to experiment 1, only one series of peaks can be observed in this spectrum. The main peak was found at m/z 549.3. The mass of this peak is 6 Da heavier compared to the monoisotope peak of the unlabeled peptide, and 6 Da lighter compared to the monoisotopic peak of the 15N labeled peptide. This indicates that the main peak consists of peptides that contain six 14Nitrogens and six 15 Nitrogens. The distribution around the main peak appears to be Gaussian, meaning that 15Nitrogen is randomly distributed throughout the protein population. This is in agreement with full adaptation of a culture to a medium with a RIA of 50%. The protein population can be considered homogeneous, since it solely consists of labeled peptides. Both experiments 1 and 2 were designed to allow for the introduction of 50% 15Nitrogen into the biomass. However, the design of the experiments was fundamentally different and resulted in completely distinct mass spectra. To describe the differences between both experiments, we make a distinction between and discuss the parameters labeling efficiency and incorporation efficiency. In the literature, no clear distinction or convention appears to be made about the meaning of these parameters. Here, we define labeling efficiency as the percentage of peptides with the same amino acid sequence that contain a 15N label labeling efficiency (% ) )

labeled peptides × 100 labeled + unlabeled peptides

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Note that an unlabeled peptide in this case contains the natural abundance of 14N and 15N and a labeled peptide is enriched in 15 N. A labeling efficiency of 100% can be achieved by adapting a cell culture for several doubling times (>8) to a heavy isotopecontaining medium. The labeling efficiency can be calculated by determining peak areas of labeled and unlabeled peptides in TOF-MS spectra. The incorporation efficiency on the other hand is the percentage of 15N atoms in each peptide: incorporation efficiency (% ) ) no. of 15N atoms × 100 no. of N atoms + no. of 14N atoms 15

The incorporation efficiency is ultimately dependent on the RIA of the medium, and when a cell culture has fully adapted to a medium; the incorporation efficiency and the RIA will be equal. An incorporation efficiency of 100% is very difficult to achieve, because impurities in the medium are nearly unavoidable. Incorporation efficiencies of < 100% lead to “satellite” peaks at -1 Da, -2 Da, etc from the 15N monoisotopic peak, thereby potentially complicating the quantification process. Here, we calculated the incorporation efficiency with the least-squares method on the basis of comparison of theoretical and experimental spectra. For each peptide, the minimum in a MSE plot was determined. The incorporation efficiencies for the unlabeled and labeled peptides from Figure 2A were determined as 0.36% and 99.5% (Figure 2B). The unlabeled peptides reflect the incorporation due to natural occurring isotopes (0.36%), whereas the labeled peptides reflect the RIA of the 15NH4+ containing medium (>98%). The labeling efficiency on the other hand was determined as 50% on the basis of peak areas. This value corresponds well with the expected value of 50% that can be expected when labeled and unlabeled proteins are mixed in the ratio 1:1. The incorporation efficiency for the same peptide in experiment 2 was determined as 47.5% (Figure 3). This approaches the value of 50% expected in a medium with a RIA of 50% (the possibility of a biological bias toward isotopes is not discussed in this paper). The labeling efficiency on the other hand is ∼100% since all peptides are labeled (enriched in 15N) and the unlabeled peak has become indistinguishable from background signal (Figure 3A). Therefore, the peptide population in experiment 2 can be regarded as homogeneous. Isotope and Biological Perturbation. The design of experiments 3 and 4 was fundamentally different from experiments 1 and 2. In experiments 3 and 4 the medium was changed halfway the growth experiment. After this, cells were allowed to grow for one more doubling time. The result is that half the biomass was created in the presence of the 14N medium and half in the 15N medium. Amino acids that are synthesized through ammonium assimilation will contain 15Nitrogen and amino acids that are recycled will contain 14Nitrogen. Therefore, the incorporation efficiency is a measure of the relative contribution of both nitrogen pools to protein synthesis. The labeling efficiency on the other hand, is a measure of protein degradation (turnover). For experiments 3 and 4, two extreme cases can be hypothesized. (I) When protein degradation does not take place and protein synthesis is entirely dependent on ammonium assimilation, a TOF-MS spectrum resembling Figure 2 will be observed. The labeling efficiency in this case is 50% and the incorporation 2188

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efficiency of the newly synthesized proteins will reflect the RIA of the medium (99.5%). (II) In case all proteins are degraded and amino acids are recycled, ammonium assimilation will only be responsible for 50% of the newly synthesized proteins. Therefore, a TOF-MS spectrum resembling Figure 3 will be observed. The labeling and incorporation efficiencies in this case will be 100% and 50% respectively. The design of experiments 3 and 4 was slightly different. Experiment 3 was designed to allow for isotope perturbation and experiment 4 was designed to allow for both isotope and biological perturbation. Isotope perturbation was achieved by changing the nitrogen source from 14NH4+ to 15NH4+. This change has no biological implications, since metabolism of 14 NH4+ and 15NH4+ involves the same set of proteins. Over time, the isotope forms will become randomly distributed throughout the system because of chemical (diffusion) and biological processes (protein synthesis and degradation). The biological system itself can be perturbed by a change in physical (pH, temperature) or chemical (nutrients, salts) conditions. The system will respond to this kind of perturbation with a proteomic “shift” in order to adapt to the new conditions. In experiment 4, a biological perturbation was applied by a change in carbon source from D-glucose to D-arabinose. A proteomic shift involves synthesis of a set of new proteins and the degradation of a set of redundant or harmful proteins, possibly affecting the distribution of nitrogen throughout the biomass. Figure 4 shows the effect of experiments 3 and 4 on the isotope distribution pattern of the example peptide FIAEGAELNK. The spectra appear to represent an intermediate situation compared to the spectra in Figures 2 and 3. In both cases, the areas of the unlabeled peaks are smaller than the labeled peaks, providing evidence that protein degradation takes place but is incomplete. The protein population is still heterogeneous, since labeled and unlabeled forms of the protein coexist. The incorporation efficiencies clearly exceed 50%, but have not reached the RIA value of 99.5%, thus providing evidence for amino acid recycling. Ammonium assimilation appears to be the main mechanism for protein (amino acid) synthesis since the incorporation efficiency is closer to 99.5% than to 50% (93% and 91% for experiments 3 and 4 respectively). In each experiment, labeled and unlabeled peptides appeared in two clearly distinct series of peaks that are suitable for quantitation (Figure 4). By definition, labeling cannot be complete as long as there are unlabeled peptides present, because it means 14N and 15N are not yet randomly distributed throughout the system (biomass and medium). At each point in time after the medium replacement, the system is moving toward this equilibrium. This means that the 14N/15N composition of the nitrogen pools is constantly changing. The isotope distribution pattern of each protein is dependent on the microenvironment (pool composition) at the time of synthesis. Ultimately, the isotope abundance of the nitrogen pools will reflect the RIA of the medium. The Yergey algorithm assumes full adaptation to a certain RIA. As long this is not the case, there will be a certain error between theoretical and experimental spectra (this error is also affected by chemical and instrument generated noise). Despite the above considerations, MSE plots gave clear minima (Figure 5) and quantitation can be performed as long as labeled and unlabeled peptides appear in two distinct series of peaks. In other words, incomplete

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Figure 5. MSE plots for three different peptides corresponding to Thermosome subunit A.

Figure 6. Labeling and incorporation efficiencies for five different proteins in experiments 3 and 4. ThsA ) Thermosome subunit A, ThsB ) Thermosome subunit B, GlyA ) Serine/glycine hydroxymethyltransferase, TUF ) translation elongation factor Tu homologue, GdH ) Glutamate dehydrogenase.

Figure 4. (A) and (B) TOF-MS spectra of the example peptide FIAEGAELNK obtained from experiments 3 and 4, respectively. Insets provide theoretical spectra calculated on the basis of the MSE plot (93% and 91% for experiments 3 and 4 respectively). In both cases there is a significant abundance of unlabeled peptides. The labeling efficiencies in experiment 3 and 4 were 81% and 72% respectively. (C) MSE plots.

labeling is not a problem and 15NH4+ labeling can be used in protein turnover studies. Incomplete labeling in SILAC approaches is problematic because usually only one amino acid residue per peptide is labeled. Unlabeled amino acids in the growth medium lead to unlabeled peptides in samples that are presumed labeled, therefore complicating the quantification. Figure 6 shows the incorporation and labeling efficiencies for five different proteins. In each case the calculations were performed for three distinct peptides corresponding to the same protein. Labeling efficiencies were always between 50% and 100%, indicating that protein degradation always took place but was incomplete. Labeling efficiencies ranged from 63% for TUF in experiment 4 to 94% for GdH in experiment 3. Proteins with low labeling efficiencies are more stable com-

pared to proteins with high labeling efficiencies. Therefore, it can be concluded that TUF is the most stable and GdH is the least stable protein in this dataset. The stabilities of ThsA, ThsB, and GlA are intermediate. Protein turnover was consistently higher for proteins from experiment 3 compared to experiment 4. Protein turnover was expected to have a negative effect on the incorporation efficiency because of (14N) amino acid recycling. Despite this, the incorporation efficiency was consistently higher in experiment 3 compared to experiment 4 (although marginal). From this it could be concluded that amino acid recycling is a more important mechanism in experiment 4 compared to experiment 3. Rather than recycling, the cell might simply discard degraded peptides or amino acids from the cell. Although this might be energetically inefficient, energy might not be limited during cell growth under the chosen experimental conditions. Moreover, the negative effects of the energy burden caused by redundant or inactive proteins might be greater than the benefits that amino acid recycling presents. However, speculation on this is beyond the scope of this study and requires additional experimental data. Future studies on energy or nitrogen starvation might provide more insight into this matter. Journal of Proteome Research • Vol. 4, No. 6, 2005 2189

research articles Another observation from Figure 6 is that labeling efficiency is a parameter that is protein specific, whereas incorporation efficiency is similar throughout the protein population. This can be explained from a physiological point of view. Labeling efficiency is related to protein degradation (turnover). This parameter is protein-specific and depends on the stability of each particular protein in the proteome. Incorporation efficiency on the other hand is related to protein synthesis. Since all proteins are synthesized from the same nitrogen pool, the incorporation efficiency for all newly synthesized proteins is expected to be similar. Mass Balances. Despite the fundamental difference between labeling and incorporation efficiency a connection between them exists, since they both describe the distribution of nitrogen through the system. In experiments 1 and 2, two extreme distributions were created (heterogeneous vs homogeneous protein population). In experiments 3 and 4, it was shown that in reality an intermediate situation occurs. Mass balances can be a useful tool to quantify the contribution of each nitrogen pool to protein synthesis. The 15Nitrogen content of the biomass can be calculated by multiplying the labeling efficiency by the incorporation efficiency. In the case of experiment 1, this becomes: (50% × 100%) ) 50%. In experiment 2 this is: (100% × 47.5%) ) 47.5%. In both cases this reflects the expected value of 50%, since in both cases 50% of the biomass contains 15Nitrogen. A number of problems complicate the set up of accurate mass balances. (I) Labeling efficiencies represent relative protein expression values, whereas absolute values are required in mass balances. (II) The mass balance should contain the complete expressed proteome; however, with the current separation and detection methods only partial proteome coverage can be achieved.22 (III) Other potential 14Nitrogen sources such as intracellular 14NH4+ and other nitrogen-containing biomolecules are not accounted for. Nevertheless, a number of general observations and conclusions can be made based on the incorporation and labeling efficiencies from Figure 6. A meaningful 15N content calculation can be made if it is assumed that the incorporation and labeling efficiencies of the five proteins from Figure 6 are representative throughout the proteome. Average labeling and incorporation efficiencies were 81.8% and 92.2% (experiment 3) and 73.0% and 89.2% (experiment 4). On this basis, a 15N content of 75% (81.8% × 92.2%) and 65% (73% × 89.2%) for experiments 3 and 4 was calculated. In both cases, the15N content of the biomass clearly exceeds 50% providing evidence that degraded proteins are indeed discarded from the cell. These results indicate that under the provided assumptions elemental mass balances can provide quantitative, meaningful results. Isotope Distribution Patterns in Database Searching. In the previous section, experimental spectra of known peptides with known elemental compositions were matched to theoretical spectra to determine incorporation efficiencies. However, a reverse strategy can be applied that is potentially useful in protein identification and database searching. In this case, the isotope distribution pattern is used to determine the elemental composition of a peptide.23 Often peptides have very similar accurate masses but differ significantly in their elemental composition. Mass spectrometers can only measure peptide masses within a certain mass accuracy. In general, it is difficult to distinguish between peptides that have the same nominal mass. The nominal mass of a peptide is the total number of protons and neutrons present in the peptide. The contributions 2190

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Figure 7. MSE minima for 20 different peptides with unique elemental composition and a nominal mass of 1074 Da. The error was calculated with an experimentally obtained spectrum corresponding to the peptide SATEAATAILK. The theoretical spectrum of this peptide was the third best match to the experimental spectrum.

to the nominal mass of the elements C, H, N, O, and S are 12, 1, 14, 16, and 32, respectively. An in silico tryptic digest on the S. solfataricus proteome (ftp://ftp.ncbi.nih.gov/genomes/Bacteria/ Sulfolobus_solfataricus/) was performed with Proteogest software.24 This yielded a total of 20 peptides with unique elemental composition and a nominal mass of 1074 Da. The MSE was calculated for each of these peptides compared to an experimental spectrum corresponding to a peptide with the sequence SATEAATAILK (based on the MS/MS spectrum, data not shown). Figure 7 shows that the theoretical spectrum of SATEAATAILK ranked third among 20 MSE values. This indicates that MSE calculations can aid in peptide identification, but used alone are unlikely to result in unambiguous peptide assignment. Additional experimental data in the form of MS/ MS spectra or multiple peptides per protein are required. Moreover, MSE values are dependent on the quality (ion statistics) of the spectra. MS Spectra are often complicated by chemical and instrumental noise. MS/MS spectra often show a lesser degree of noise and therefore MSE calculations on peptide fragments might provide additional confidence.

Concluding Remarks Stable isotope labeling through metabolic labeling is an excellent method to manipulate peptide masses, and mass spectrometry is an excellent tool to measure peptide masses.25 Here, we have used a number of unconventional labeling strategies such as incomplete labeling and medium replacement to perturb isotope distribution patterns in proteins. The discussion mainly focused on the applications and limitations of 15NH4+ labeling. In addition, it was shown that 15NH4+ labeling can be used to obtain insight into the dynamics of protein synthesis and degradation in the hyperthermophilic crenarchaeon S. solfataricus. Unlike the genome, the proteome is not static but dynamic. One of the challenges in future proteomic studies is to gain insight into the dynamics of proteome changes. This study showed that stable isotope labeling with 15NH4+ in combination with mass spectrometric analysis provides an excellent tool to conduct these kinds of dynamic proteomic studies.

Acknowledgment. We thank the University of Sheffield and United Kingdom’s SRIF Infrastructure funds for support. A.P.L.S. thanks the University of Sheffield and the EPSRC for a

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scholarship. B.K. thanks the European Union’s ERASMUS Program. P.C.W. thanks the UK’s Engineering and Physical Sciences Research Council (EPSRC) for provision of an Advanced Research Fellowship (GR/A11311/01) and for funding (GR/S84347/01).

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