Determination of Fractional Synthesis Rates of Mouse Hepatic

Determination of Fractional Synthesis Rates of. Mouse Hepatic Proteins via Metabolic. 13C-Labeling, MALDI-TOF MS and Analysis of. Relative Isotopologu...
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Anal. Chem. 2005, 77, 2034-2042

Determination of Fractional Synthesis Rates of Mouse Hepatic Proteins via Metabolic 13 C-Labeling, MALDI-TOF MS and Analysis of Relative Isotopologue Abundances Using Average Masses Josef A. Vogt,†,‡ Christian Hunzinger,‡,§ Klaus Schroer,§ Kerstin Ho 1 lzer,§ Anke Bauer,§ § § § Andre´ Schrattenholz, Michael A. Cahill, Simone Schillo, Gerhard Schwall,§ Werner Stegmann,*,§ and Gerd Albuszies†

Universita¨tsklinikum fu¨r Ana¨sthesiologie, Universita¨t Ulm, Sektion APV, Parkstrasse 11, 89075 Ulm, Germany, and ProteoSys AG, Carl Zeiss Strasse 51, 55129 Mainz, Germany

Proteins of a liver extract taken from a metabolically 13Clabeled mouse were separated by 2D-PAGE and identified after tryptic digestion by MALDI-TOF MS peptide mass fingerprinting. 13C-Labeling of proteins was achieved by an infusion of U-13C-glucose, which is metabolized to labeled nonessential amino acids. The labeling was analyzed using the relative isotopologue abundances of the measured isotope pattern of tryptic peptides and quantified by their increase in the average molecular mass (∆AVM). Fractional synthesis rates (FSR) of proteins were determined from corresponding peptides using measured ∆AVM values as well as ∆AVM values deduced from tRNA-precursor amino acid labeling, which in turn was derived from proteins showing high 13C enrichments. The 8-h FSR values of 43 proteins were determined to range from 0 ( 0.6 to 95 ( 1%/8 h, with typical errors given as SEM values, which depend on the number of peptides of a specific protein usable for calculation. The method demonstrates that FSR values as an indicator for protein turnover in the liver proteome can be estimated within narrow error margins, providing baseline values from which treatment-dependent deviations could be detected with high statistical certainty. A variety of factors control the cellular abundance of individual protein species, including transcription, translation, posttranslational modification, and degradation or cellular export. The individual disappearance rates vary over a large range and are regulated independently from transcription or translation.1 Therefore, protein abundance levels alone are insufficient to characterize the activity of the various regulatory processes involved; additional determination of synthesis or disappearance is required. * Corresponding author: E-mail: [email protected]. † Universita¨t Ulm. ‡ These authors contributed equally to this work. § ProteoSys AG. (1) Glickman, M. H.; Ciechanover, A. Physiol. Rev. 2002,. 82, 373-428.

2034 Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

Protein synthesis can be measured by the incorporation of isotope-labeled amino acids. This depends on two factors: first, the labeling on the precursor amino acids, and second, on the fraction of a specific protein or a mixture of proteins that was de novo synthesized during the labeling period. This fraction is termed “fractional synthesis rate” (FSR) and is usually taken as a measure for protein synthesis and turnover. FSR values are calculated from isotope incorporation and the isotope content of the amino acid precursors. These precursors are bound to acyltRNA, and their pools exchange with pools linked to other processes such as biosynthesis of amino acid or their release by proteolysis. These other pools may have different isotope labeling and cannot be used as a surrogate measurement to determine precursor labeling. A direct measurement of this labeling on isolated acyl-tRNA is technically possible, albeit cumbersome and requiring a large sample amount due to low tissue content.2 To summarize the present state of affairs in this very large field, most direct or indirect approaches for accurate estimation of protein precursor labeling are associated with inherent and long-standing difficulties.3-5 In one example strategy the precursor labeling is estimated from the labeling on a protein, which is not diluted by unlabeled material and therefore should reflect the tRNA labeling. Plasma lipoprotein ApoB100 is such a protein. It has a high turnover rate and was therefore proposed as a site to measure precursor labeling.6 Ahlman et al.,7 however, failed to achieve 100% fractional synthesis for pigs within 8 h. (2) Baumann, P. Q.; Stirewalt, W. S.; O’Rourke, B. D.; Howard, D.; Nair, K. S. Am. J. Physiol. 1994, 267, E203-E209. (3) Davis, T. A.; Reeds, P. J. Curr. Opin. Clin. Nutr. Metab. Care 2001, 4, 5156. (4) Garlick, P. J.; Wernerman, J.; McNurlan, M. A.; Essen, P.; Lobley, G. E.; Milne, E.; Calder, G. A.; Vinnars, E. Clin. Sci. 1989, 77, 329-336. (5) Smith, K.; Downie, S.; Barua, J. M.; Watt, P. W.; Scrimgeour, C. M.; Rennie, M. J. Am. J. Physiol. 1994, 266, E640-E644. (6) Reeds, P. J.; Hachey, D. L.; Patterson, B. W.; Motil, K. J.; Klein, P. D. J. Nutr. 1992, 122, 457-466. (7) Ahlman, B.; Charlton, M.; Fu, A.; Berg, C.; O’Brian, P.; Nair, K. S. Diabetes 2001, 50, 947-954. 10.1021/ac048722m CCC: $30.25

© 2005 American Chemical Society Published on Web 03/03/2005

Theoretically, the precursor labeling and the synthesis rates can be determined simultaneously8 by incorporating a tracer at two or more positions within a single molecule and measuring the resulting enrichments of single- and double-labeled molecular fragments. Following this principle the synthesis of plasma albumin was analyzed by Papageorgopoulos et al.9 From the precisely measured mass distribution of a single peptide of albumin, both the precursor labeling and the FSR were determined, without using any other measurements. However, proteins with a half-life larger than 10 days cannot be measured by this approach,10 even when employing a labeling period of up to 24 h. In the present study we extend these principles to determine the synthesis rate of multiple proteins as a prerequisite to be able to characterize changes in the turnover of proteins in living animals. Using 2D-PAGE and MALDI-TOF we already identified a large number of different tissue proteins and showed that the distributions of relative isotopologue abundances (RIA) of peptides provide sufficiently accurate and precise measurements of isotope patterns to detect changes in stable isotope composition.11 The 2D-PAGE/MALDI-TOF technique provides the mass distribution for many different peptides obtained under the same labeling conditions and, by means of peptide mass fingerprinting, the amino acid sequence for these peptides. This wealth of information, available from the protein extract from one labeled and one unlabeled sample, should be sufficient to determine the various FSR values and the degree of precursor labeling. We want to assess changes in the synthesis of many different proteins under natural conditions, which imposes some restraints. A significant rise in the plasma concentration of precursors should be avoided, because amino acids are likely to interfere with metabolism via direct signaling effects.12,13 This avoidance might be difficult considering the following physiological constraints: Amino acids generally have a large turnover due to protein synthesis and breakdown, with only a small fraction being oxidized, i.e., ∼10% for leucine.14 A tracer infusion of the size of the basal oxidation rate thus doubles the amount that must be removed by oxidation and hence is likely to substantially increase the plasma concentration of the combined labeled and unlabeled amino acid. On the other hand this infusion rate is still small compared to the breakdown rate and is accompanied with a small enrichment value, because the breakdown dilutes the infused tracer. Considering regulation processes that counteract the expected increase in plasma concentration, one may be able to infuse more than the aforementioned 10% of the breakdown rate and the upper limit achievable for precursor enrichment of amino acids such as leucine will be ∼20%. Hence, if only one specifically labeled precursor amino acid such as leucine is used in physi(8) Strong, J. M.; Upton, D. K.; Anderson, L. W.; Chisena, C. A.; Cysyk, R. L. J. Biol. Chem. 1985, 260, 4276-4281. (9) Papageorgopoulos, C.; Caldwell, K.; Shackleton, C.; Schweingrubber, H.; Hellerstein, M. K. Anal. Biochem. 1999, 267, 1-16. (10) Papageorgopoulos, C.; Caldwell, K.; Schweingrubber, H.; Neese, R. A.; Shackleton, C. H.; Hellerstein, M. Anal. Biochem. 2002, 309, 1-10. (11) Vogt, J. A.; Schroer, K.; Ho ¨lzer, K.; Hunzinger, C.; Klemm, M.; BiefangArndt, K.; Schillo, S.; Cahill, M. A.; Schrattenholz, A.; Matthies, H.; Stegmann, W. Rapid Commun. Mass Spectrom. 2003, 17, 1273-1282. (12) Kimball, S. R.; Jefferson, L. S. Curr. Opin. Clin. Nutr. Metab. Care 2002, 5, 63-67. (13) Lynch, C. h. J.; Patson, B. J.; Anthony, J.; Vaval, A.; Jefferson, L. S.; Vary, T. C. Am. J. Physiol. Endocrinol. Metab. 2002, 283, E503-E513. (14) Meguid, M. M.; Matthews, D. E.; Bier, D. M.; Meredith, C. N.; Young, V. R. Am. J. Clin. Nutr. 1986, 43, 781-786.

ological concentrations, the labeling of proteins with low synthesis rates will be only marginal. Because we aim to determine fractional synthesis rates of multiple proteins, it is essential to measure FSR values of e10%/8 h. Under these circumstances less than 10% of the proteins will be labeled with a labeling degree of ∼20% on each labeling position, which results in less than 2% labeling. This is hardly quantifiable by conventional MALDI-TOF MS approaches. Thus, protein labeling by administration of a single labeled amino acid such as leucine cannot be used for our approach. Wykes et al.15 infused pigs with U-13C-glucose and observed a significant labeling in those amino acids in close exchange with the keto acids of the Krebs cycle. A similar transfer of 13C-labeling on glucose/lactate to amino acids was also observed for a perfused rat liver.16 We followed these approaches and infused labeled glucose into mice. For mice it is feasible to cover the total energy requirement with labeled glucose. This should yield a high degree of labeling in the amino acids connected to glucose metabolism, without interfering with their metabolism. Moreover, compared to larger animals the mouse has a higher metabolic rate, promising an efficient protein labeling within a few hours. Metabolic labeling via glucose generates complex labeling patterns on the nonessential amino acids and even more complex patterns on labeled peptides. This makes it difficult to link a measured peptide pattern to the labeling pattern on its constituent amino acids. Hence, we only consider the average mass of an amino acid or a peptide. Average mass is a robust measure for the molecular mass17 and can be used in isotope dilution experiments.18 It increases, i.e., shows a positive mass shift, with the degree of labeling with heavy isotopes. However, the mass shift does not contain enough information about the mass distribution to allow for a joint determination of precursor labeling and fractional synthesis rate, as is possible with the approach of Papageorgopoulos.9 Nevertheless, the mass shift on the precursor amino acid can be linked to the mass shift of peptides from liver proteins and their fractional synthesis rate: Given the precursor mass shifts derived from hepatic proteins, and using the amino acid sequences of peptides predicted by peptide mass fingerprinting, all necessary information is available to calculate the average mass of a peptide from a de novo synthesized protein. A measured peptide mass shift of a protein at the end of a labeling phase is inversely related to the fractional synthesis. This interrelation between precursor mass shift, measured peptide mass shift, and fractional synthesis rates is used in two different ways: First, the precursor labeling is assessed from the mass shift of peptides from hepatic secretion proteins with a fractional synthesis rate that can be reasonably set as being close to 100%. This step is comparable to the strategy discussed earlier, where the secreted protein lipoprotein ApoB100 in plasma6 was taken as the primary source for labeled precursors. In a second step, the estimated amino acid precursor labeling from measured mass shift values of other peptides is used to assess the fractional synthesis of their parent proteins. (15) Wykes, L. J.; Jahoor, F.; Reeds, P. J. Am. J. Physiol. 1998, 274, E365E376. (16) Fernandez, C. A.; Des Rosiers, C. J. Biol. Chem. 1995, 270, 10037-10042. (17) Yergey, J.; Heller, D.; Hansen, G.; Cotter, R. J.; Fenselau, C. Anal. Chem. 1983, 55, 353-356. (18) Blom, K.; Dybowski, C.; Munson, B.; Gates, B.; Hasselbring L. Anal. Chem. 1987, 59, 1372-1374.

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In this paper we explore whether the precision of MALDITOF mass spectrometry is sufficient to detect and usefully quantify peptide mass shifts from proteins of metabolically in vivo labeled mice and thereby to permit assessment of the fractional synthesis and turnover rates of multiple proteins. We introduce a theoretical consideration and practical demonstration of the analysis of relative isotopologue abundances to quantify the increase in average molecular ion mass. We also examine the suitability of a labeling regime of 8 h, determining the minimal fractional synthesis that can be detected under these circumstances. MATERIALS AND METHODS Our raw data were obtained by 2D-PAGE, tryptic digestion, and MALDI-TOF measurements of proteins from a liver extract from an unlabeled and a 13C-glucose labeled mouse. They consist of the mass distributions of tryptic peptides together with their amino acid sequences derived from peptide mass fingerprinting analysis. In the following section we provide the definitions and equations necessary to derive the FSR values from these raw data. First we calculate the gain in the average mass of a peptide, generated by the incorporation of 13C isotopes, which is denoted as peptide mass shift. We then show how the mass shift of peptides can be used to assess the fractional synthesis rates of its parent protein, if the mass shifts of the labeled amino acid precursors are given. We then outline how the mass shifts of the amino acid precursors can be determined using the peptide mass shifts of a reference protein that exhibits a FSR value of almost 100%/8 h. Figure 1 schematically depicts the processes involved. Finally, we show how the FSR values that pertain to an 8-h labeling period are related to FSR values with standard time units and to the half-lives of the proteins. Definitions and Calculations. RIA Values. As described previously11 RIA values were defined as the relative number of all isotopologues (same chemical structure, but different isotopic composition) with the same mass offset j of a peptide expressed as the fraction of all mass spectrometrically determinable isotopologues of this peptide. Accordingly, the RIA value of an Mj isotopologue of a particular peptide fragment f of a protein is given by

∑S

RIAj,f ) Sj,f/

(1)

j,f

where Sj,f (j ) 0, ..., n) are the mass spectrometrically measured ion signals derived from an isotope pattern of the n peaks of an isotopologue distribution of a peptide f. The ion signals are determined as integrals over baseline-corrected peaks defined by their full width at half-maximum. Average Mass. The average mass (AVM) of a peptide f is defined as n

AVMf )

∑ M (RIA j

j)0

n

j,f)

)

∑(M

o

+ j)(RIAj,f) )

j)0

n

Mo +

∑M (RIA j

j,f)

(2)

j)0

with Mj(RIAj,f) characterizing the contribution of the Mj isotopologue to the AVM. 2036

Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

Figure 1. Outline for the determination of the mass shifts of labeled precursor amino acids and fractional synthesis rates via nonlinear regression analysis. The mass shifts of the peptides can be determined from the measured mass distributions (right branch) as well as be predicted using the amino acid sequences of the peptides, the FSR values of their parent proteins, and the mass shifts of the precursor amino acids (left branch). The predicted and measured peptide mass shifts are compared and, if they fail to achieve the matching criteria, are adjusted. The dotted loop indicated in the lower left part of the figure is iteratively traversed until an optimal congruence between predicted and measured peptide mass shift values is found. The nonlinear regression procedure provides the direction of change for the adjustment steps and also performs the test for optimal congruence.

Mass Shift. The average mass of a compound is shifted via labeling with heavier isotopes toward higher values. The mass shift ∆C of a labeled compound C is defined as the difference between the AVM of the labeled and the natural (i.e., unlabeled) compound:

∆C ) AVM(Clabeled) - AVM(Cnatural)

(3)

Precursor Labeling. Amino acids bound as acyl-tRNA are used for protein synthesis. These bound amino acids are referred to as precursors. The labeling of an amino acid in the precursor pool is denoted as precursor labeling and expressed as the mass shift ∆k, where k denotes a specific amino acid. Fractional Synthesis Rate. The FSR or turnover rate of a protein is expressed as percentage of the newly produced fraction of this protein during the labeling phase. The mass shift of a peptide f emanating from a labeled protein p is directly proportional to FSRp according to

∆f(FSR,∆k) ) FSRp

∑N

(4)

k,f∆k

k

The proportional coefficient ∑kNk,f∆k reflects the mass shift of a peptide from a de novo synthesized protein and is based on the precursor labeling. Nk,f gives the number of occurrences of labeled amino acid k in the amino acid sequence of the peptide. The mass shift of a peptide f can also be determined using the measured RIA values according to

∆f(RIA) )

∑ M (RIA (labeled) - RIA (natural)) j

j

j

(5)

FSR(obs) ) 100(1 - e-κt1)

j

For known precursor labeling the fractional synthesis rate of a protein FSRp can be calculated combining eqs 4 and 5 according to

FSRp ) ∆f(RIA)/

∑N

(6)

k,f∆k

k

Determination of the Precursor Labeling. The measured mass shift values ∆f(RIA) of peptides should match the corresponding values ∆f(FSR, ∆k), predicted by the fractional synthesis rate FSR and considering the mass shift contribution ∆k for all individual amino acids in the peptide. Based on this requirement the precursor mass shift values are determined with a nonlinear regression procedure. Its concept and single steps are outlined in Figure 1. In addition to the equations shown above, the nonlinear regression requires a measure for the congruence between measured and predicted peptide mass shift values. The latter is quantified as the sum of squared differences between measured and predicted mass shift values, each weighted against the measurement error of the peptide mass shift and denoted as the congruence indicator Q:

(

∆f(RIA) - FSRp

Q)

∑ f

∑N k

σ∆f

k,f∆k

)

Hence, we must select a reference protein for which a reasonably accurate estimation of FSRp value can be made. To minimize the bias caused by this deliberate choice in this study we chose a reference protein with a turnover that is high enough to produce an almost 100% fractional synthesis within the given labeling period. Conversion of FSR to Half-Lives. The duration of the labeling period was 8 h, and we express FSR values of newly synthesized proteins in the time unit of the labeling period, i.e., %/8 h. The FSR values depend on the duration of the labeling period, t1, as follows:

2

(7)

Here σ∆f denotes the measurement error in the peptide mass shift. It is calculated from conventional error propagation19 starting from area measurements Si,f, which are used in eq 1 to calculate RIA values to the final eq 5 for the peptide mass shift. The propagated absolute errors range from 0.05 to 0.25 Da. The underlying error in area determination is assessed from the signal-to-noise ratio comparable to earlier error estimates in RIA determinations.11 The fractional synthesis rate FSR values of proteins have to be determined by regression together with the precursor labeling. Only a few selected intensively labeled proteins need to be used. Unfortunately, FSRp values for these proteins must be determined relative to the value of a reference protein. For the reference protein we must estimate a reasonable FSRp value, and all other FSRp values then pertain to and are scaled by this reference. (19) Press: H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical recipes in C++, 2nd ed.; Cambrigde University Press: Cambrigde, 2002; Chapter 15.

(8)

where the rate constant κ gives that fraction of the protein moiety, that is degraded per hour. Equation 8 can be solved for k as

κ ) ln

/t (100100 - FSR)

1

The half-live, T1/2, expressed in days, a conventional measure for protein turnover, can then be assessed as

T1/2 ) ln(2)/κ/24

(9)

Equation 8 holds only under the simplifying assumption of stationary conditions for the proteins in question, such that their breakdown rates equal their production rates and that first-order kinetics applies to the breakdown process. These assumptions cannot be uniformly hypothesized for the many different proteins analyzed here because little is known about their kinetic behavior, and therefore, a unit conversion of the FSR values via eq 8 from (%/8 h) into standard units such as (%/h) or (%/d) could generate erroneous results and is not recommended. Mice Experiments. Male mice of the C57BL/6 strain were employed. After an overnight fast one mouse obtained an intraperitoneal injection of ketamine (150 µg/g), midazolam (1 µg/ g), and atropine (0.5 µg/g). Then a central venous custom-made catheter (PTFE tubing 0.4-mm o.d.) was placed in the right jugular vein for a continuous infusion of U-13C-glucose, fentanyl (0.2 µg/ g‚h), and ketamine (20 µg/g‚h). U-13C-Glucose, i.e., glucose labeled uniformly with 13C at all six positions, was obtained from Campro-Scientific GmbH (Berlin, Germany). A custom-made miniaturized combined conductance catheter-micromanometer (Millar, Houston, TX) was placed via the right carotid artery in the left heart ventricle to monitor local pressure and volume and systemic arterial pressure. After 1-h recovery the glucose tracer infusion was started with 2 mg/g‚h over 30 min followed by 1 mg/g‚h and continued over 10 h, which results in an effective labeling phase of 8 h, accounting for an initial transient phase of 2 h. The mean arterial pressure was stable and at 80-90 mmHg and the heart rate was at 450-600/min. Then the mouse was sacrificed and the liver immediately excised and stored at -80 °C. A control liver was excised from a second nonlabeled mouse immediately after killing. GC/MS Analysis of Free Metabolites of the Liver Extract. To assess the labeling efficiency, the liver extract was also Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

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analyzed by GC/MS. About 70 mg of frozen liver was powderized, extracted with methanol, and centrifuged. The resulting supernatant was lyophilized, redissolved in 0.1 N HCl, and applied to an ion exchange column (Dowex 1 × 2-100) to separate glutamate from glucose and alanine. Alanine was measured as tert-butyldimethylsilyl derivatives20 in the mass range m/z 260264 under electron impact ionization using a Fisons GC 8000/ MD 800 GC/MS system. Glutamate and glucose were measured under chemical ionization using an Agilent 6890GC/5973 MS system: glutamate as an n-propyltrifluoroacetic acid derivative20 in the mass range m/z 328-334 and glucose as an aldonitrilepentazetate derivative20 in the mass range m/z 328-335. The resulting mass distribution for each metabolite was then used to calculate the mass shift with eqs 2 and 3. Peptide Mass Fingerprinting Using MALDI-TOF MS. 2DPAGE. Mouse liver (100 mg) was ground under liquid nitrogen and subsequently extracted in 1.2 mL of IEF buffer (7 M urea, 2 M thiourea, 4% CHAPS, 1% Triton X-100, 10% (v/v) glycerol, 65 mM DTT). Protein concentration was determined using a modified Bradford assay adapted from Ramagli and Rodriguez.21 A 200400-µg sample of protein was loaded onto IPG strips, pH 4-7, by passive rehydration. Electrophoresis was carried out as previously described.11 In-Gel Digestion. Protein spots were excised from 2D-PAGE gels using an Investigator ProPic picking robot (Genomic Solutions Ltd., Huntington, NY). In-gel digestions were performed using an Investigator ProGest digestion robot (Genomic Solutions Ltd.). Briefly, the protein spots were washed, destained, in-gel reduced at 60 °C using DTT, and in-gel digested (incubation at 37 °C overnight) with an excess of Promega sequencing grade modified trypsin. MALDI Target Preparation. The samples were loaded onto Bruker AnchorChip-Targets, i.e., stainless steel supports coated with hydrophobic material equipped with an array of 384 circular interruptions (anchors) of 600-µm diameters. All samples were prepared using R-cyano-4-hydroxycinnamic acid (HCCA) as matrix. For AnchorChip preparation 0.3 µL of analyte solution and 1.2 µL of matrix solution (0.3 g/L HCCA in ethanol:acetone ) 2:1) was applied onto the anchors using an Investigator ProMS MALDI Spotting Robot (Genomic Solutions Ltd.). Sample drying was carried out at room temperature. Mass Spectrometry. Mass spectrometric measurements were carried out on a Bruker Utraflex time-of-flight mass spectrometer (Bruker Daltonics, Bremen, Germany). The mass spectrometer was equipped with a SCOUT-MALDI source for multisample handling, a pulsed UV laser, a two-stage gridless reflector, a 2-GHz digitizer, and multichannel plate detectors for linear and reflector mode measurements. All measurements were carried out in positive ionization mode using a reflector voltage of 25 kV. Mass spectra were acquired as sums of ion signals generated by the sample irradiation with 300 laser pulses. Spectra were internally mass calibrated using trypsin autodigestion peptide signals (m/z 842.50, 1045.56, 2211.10, 2283.17) as reference values. Mass measurement accuracies were typically e50 ppm. Database Searching. For the identification of the proteins the peptide masses extracted from the mass spectra were searched (20) Knapp, D. R. Handbook of Analytical Derivatization Reactions; John Wiley & Sons: New York, 1979; Chapters 5 and 13. (21) Ramagli, L. S.; Rodriguez L. V., Electrophoresis 1985, 6,. 559-563.

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against the NCBI nonredundant protein database (www.ncbi.nlm.nih.gov, version sept. 2002) using MASCOT software version 1.8 (Matrix Science, London). The search was performed using carbamidomethyl as a fixed modification of the cysteines, oxidation as an optional modification of methionines, one allowed missed cleavage, and a mass accuracy of 75 ppm. RESULTS AND DISCUSSION An approach to determine the fractional synthesis rates of individual proteins is presented providing a basis for the calculation of protein turnover rates. A mouse received an infusion of U-13C-glucose over 8 h. To consider the overall efficiency of labeling we analyzed free metabolites in the liver in a preliminary step. The liver was excised and free alanine, glutamate, and glucose were extracted. These metabolites have high tissue contents and reflect the branch points for the transfer of label from glucose to amino acids. To explore the labeling efficiency, their mass distributions, measured by GC/MS, were used to derive their mass shifts yielding 3.4 Da for glucose, 1.2 Da for alanine, and 1 Da for glutamate. The mass shift of glucose split in two halves yields 1.7 Da. The carbon skeleton of one glucose half reaches pyruvate, which is in close exchange with alanine. The mass shift of 1.2 Da of alanine implies that ∼60% of the alanine/pyruvate moiety is derived from glucose and the other part stems from unlabeled sources such as amino acids from the breakdown of unlabeled proteins. Pyruvate reaches the Krebs cycle on the oxidative pathway through acetyl-CoA and R-ketoglutarate that can be transaminated to glutamate. Pyruvate can also reach R-ketoglutarate via oxaloacetate on the gluconeogenic pathway by pyruvate carboxylase activity.15,16 Glutamate has a mass shift of ∼1 Da, indicating a further dilution by unlabeled material. Nevertheless, these mass shifts are sufficient to generate measurable labeling of de novo synthesized proteins. Liver proteins from the labeled mouse and the unlabeled control mouse were separated by 2D-PAGE, and selected spots were analyzed by MALDI-TOF mass spectrometry. The results are listed in Table 1. Interesting regions of a 2D gel are pictured in Figure 2. All protein identifications given in Table 1 are detected with MASCOT peptide mass fingerprinting scores of >150. The mass spectra of the labeled and unlabeled variants of the proteins shown in Table 1 were analyzed to determine the mass shifts of their peptides. The largest mass shifts, even greater than for albumin, were obtained for major urinary protein (MUP). The MUPs are small (18 kDa) pheromone-binding proteins of male rodent urine.22 As the protein dehydrates, bound pheromone is released. MUP is a highly abundant protein, and the presence of 15 separate MUP genes, exhibiting interindividual variations, suggests that evolutionary selection operates at the levels of protein abundance and function, to enhance genetic fitness.23,24 Figure 2a shows the spot cluster of the MUP proteins, of which six spots were analyzed via peptide mass fingerprinting resulting in only two different MUP identifications, despite the high amino acid sequence coverage (>80%) of the results, reflecting the highsequence homology of the MUPs. (22) Cavaggioni, A.; Mucignat-Caretta C. Biochim. Biophys. Acta 2000, 1482, 218-228. (23) Clissold, P. M.; Bishop, J. O. Gene 1982, 18,211-220. (24) Beynon, R. J.; Veggerby, C.; Payne, C. E.; Robertson, D. H.; Gaskell, S. J.; Humphries, R. E.; Hurst, J. L. J. Chem. Ecol. 2002, 28, 1429-1446.

Table 1. Peptide Mass Fingerprint Results of 2D-PAGE Separated Spots of Mouse Liver Proteins and Their Fractional Synthesis Rates spot no.

NCBI gi no.

1 2

gi|10092608 gi|121666

3e 4e 5e 6e

gi|127531

7e

gi|13654245

FSRb (%)

SEMc (%)

2.8 0.7

0.6

5 2

major urinary proteins 11 and 8 (MUP11 and MUP8), similar to: gi|127527 major urinary protein 6 precursor (MUP 6) and: gi|20972274 major urinary protein 2 precursor (MUP 2)

88.8 88.9 88.7 88.2

1.3 1.5 2.4

5 11 2 5

major urinary protein 1

95f 94.7

1.7

9 8

0.5 0.8 0.2

proteina glutathione S-transferase, pi 2, gst p-1 glutathione peroxidase (GSHPx-1) (cellular glutathione peroxidase)

8e 9 10 11 12 13 14 15 16 17 18 19 20 21 22

gi|13096984 gi|13242237 gi|13384794 gi|13385268 gi|13385584 gi|15277547 gi|15667251 gi|20070418 gi|20149748 gi|20149758 gi|20912830 gi|20913929 gi|22128627 gi|226471

glucose regulated protein heat shock protein 8, heat shock cognate protein 70 ubiquinol-cytochrome c reductase core protein 1 cytochrome b-5 RIKEN cDNA 3110049J23 haao protein propionyl CoA-carboxylase R-subunit aldehyde dehydrogenase family 7, member A1 sarcosine dehydrogenase 3-mercaptopyruvate sulfurtransferase, e expressed in nonmetastatic cells 1 (nucleoside diphosphatase kinase) prolyl 4-hydroxylase, β polypeptide sorbitol dehydrogenase 1 Cu/Zn superoxide dismutase

9.5 15.1 1.3 10.0 0.6 3.0 1.7 2.7 0.9 0.4 2.4 5.2 4.1 0.9

23 24

gi|247242

heat shock protein hsp60, hsp60)chaperonin [mice, peptide, 573 aa]

1.5 2.4

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

gi|2499469 gi|25052136 gi|27923993 gi|28518289 gi|31981282 gi|31981722 gi|31982393 gi|34328485 gi|49868 gi|532610 gi|5915682 gi|6677739 gi|6679237 gi|6680986 gi|6753036 gi|8393186 gi|8393866 gi|9506589

peroxiredoxin 2 (thioredoxin peroxidase 1) ATP synthase, H+ transporting mitochondrial F1 complex, β subunit 60S acidic ribosomal protein P0 (L10E) protein disulfide isomerase-related protein glyoxalase 1, glyoxalase 1 regulatory heat shock 70kD protein 5 (glucose-regulated protein) epoxide hydrolase 2, cytoplasmic δ-aminolevulinate dehydratase put. b-actin (aa 27-375) cytokeratin serum albumin precursor regucalcin pyruvate carboxylase, pyruvate decarboxylase cytochrome c oxidase, subunit Va aldehyde dehydrogenase 2, mitochondrial carbamoyl-phosphate synthetase 1, ornithine aminotransferase fructose bisphosphatase 1, FBPase liver

5.2 1.9 6.4 8.6 4.1 24.7 1.61 4.9 6.4 11.6 9.9 2.8 8.3 2.8 4.3 7.8 13.3 4.4

43 44

gi|9507079

selenium binding protein 2, acetaminophen-binding protein

2.4 0.1

0.6 1.1 0.5 0.9 0.4 0.9 0.4 0.5 0.6

Nd

3 4 9 2 3 3 4 3 6 2 2 4 4 2 4 6

0.9 0.6

2 4 1 4 1 7 4 2 3 3 8 7 6 2 5 2 3 5

0.5 0.6

7 10

0.9 0.6 0.5 0.4 1.5 0.6 0.4 0.6 0.7 0.3

a All protein identifications are detected with MASCOT peptide mass fingerprinting scores >150. b Fractional synthesis rates of the proteins determined for a labeling intervall of 8 h. c Standard error of mean of the FSR given for all proteins with N > 2. d Number of peptides used for the FSR calculation. e Major urinary proteins show high sequence homology. Out of six spots two classes of MUPs have been identified, which show differences in their synthesis rates: Spots 7 and 8 shows significantly higher FSR values than spots 3-6. f The estimated FSR value of 95% from spot 7 was taken as reference value. All other FSR values were normalized to this reference.

Spots 3-6 were all identified as MUP 11 and 8, which are highly similar to MUP 2 and MUP 6, whereas spots 7 and 8 were both identified as MUP 1. Figure 3a shows the labeled and natural isotopic distributions of two tryptic peptides of MUP 11 and 8 from spot 4: FAQLCEEHGILR (123-134) at 1472.8 Da and DGETFQLMGLYGR (99-111) at 1486.8 Da. The latter is also a tryptic fragment of MUP1 from spot 7, whereas the former is not, but still appears as a strongly depleted trace in spot 7, as shown in Figure 3b. This indicates that the separation of the MUPs achieved by 2D-PAGE is not complete. A very high degree of labeling is evident for all labeled peptides of Figure 3. The labeling results in a strong enrichment of heavier masses in the mass distributions, and it is even more pronounced

Figure 2. Selected regions of a 2D-PAGE of mouse liver protein. (a) Major urinary proteins (spots 3-8); (b) selenium binding protein 2 (gi|9507079: spots 43 and 44).

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Figure 3. MALDI-TOF MS spectra of two selected tryptic peptides from MUP. Isotope pattern of peptides from proteins with natural isotopic composition are shown at the top and peptides from labeled proteins at the bottom. Amino acid sequence of peptide at 1472 Da, FAQLCEEHGILR (123-134), and at 1486 Da, DGETFQLMGLYGR (99-111). (a) Spot 4 (Figure 1a), gi 127531, MUP 11 and 8. (b) Spot 7 (Figure 1a), gi 13654245, MUP 1.

for MUP1 than for MUP(11 and 8). Therefore MUP1 from spot 7 was chosen as reference protein and its FSR value was estimated by calculating the RIA values of the monoisotopicsi.e., no 13C label containing - peaks, which declines from 0.41 for the peptides with natural isotopic composition to 0.03 for the labeled peptides. If the production of monoisotopic peptides is solely attributed to the phase before the labeling period, the remaining unlabeled fraction would then be ∼7% (as 0.07 ) 0.03/0.41). This, however, serves only as an upper limit because a minor amount of peptides synthesized during the labeling period also contribute to the monoisotopic peak. From our GC/MS measurements (data not shown) we know that only about half of each of the labeled amino acids in the precursor pool carry a 13C label; i.e., the other half is monoisotopic. Hence, roughly estimated about one-third of the observed monoisotopic peaks of the labeled MUP1 peptides stems from the de novo synthesized peptide fraction during the labeling phase and the fraction produced before the labeling phase accounts for only two-thirds (that is ∼5%) of the quoted upper limit of 7%. Hence, the fractional synthesis of this MUP1 spot can be confidently assigned as at least 95% and therefore it was taken as an almost completely labeled reference protein with a predefined FSR value of 95%/8 h. The remaining FSR values then pertain to this reference value. For the determination of the precursor labeling 80 different peptides were taken from the reference MUP spot 7, the five other MUP spots (3-6 and 8) and nine additional protein spots with high peptide mass shifts (9, 10, 12, 21, 28, 30, 34, 35, and 41). Figure 4 shows the optimal precursor mass shift values ∆k as resulting from the nonlinear regression. They are in a range of 0.3 ( 0.05-1.3 ( 0.05 Da. These were used to calculate mass shift values for peptides of known sequence, which are compared in Figure 5 with measured mass shift values. The resulting plots indicate that on average predicted mass shift values equal the measured shift values without any systematic deviation at the low 2040 Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

Figure 4. Amino acid precursor labeling. The amino acid precursor tRNA mass shifts were derived via nonlinear regression analysis (see eq 5) using the measured mass shifts of 80 peptides of known amino acid sequences from proteins showing high labeling degrees.

or high end (Figure 5a). The corresponding residuals (Figure 5b) are evenly spread over the entire range of mass shift values and show that the peptide mass shifts are well predicted by the estimated precursor labeling. The fitting indicator Q, as defined in eq 7, measures the overall difference between prediction and measurement, and with Q ) 48, it is close to the degrees of freedom of 60 and is therefore within the tolerance range defined by the total determination error for a peptide mass shift. The nonlinear regression analysis provides the precision of the determined FSR and precursor labeling values. This precision is good, as demonstrated in Figure 4, despite the limited precision of the underlying individual determinations of the peptide mass shift values. Thus, the large number of 80 peptide mass shift values used in the nonlinear regression analysis appears to compensate the limited precision in individual peptide mass shift values. The good precision values for the determined precursor labeling values reflect the consistency of the data set and together with the overall quality of fit they confirm the feasibility of our approach.

Figure 5. Comparison of predicted and measured peptide mass shift values. (a) Predicted mass shifts of peptides calculated for known amino acid sequences using the amino acid precursor labeling shown in Figure 3 are plotted against their measured mass shift values. The scatterplot perfectly represents a regression line with r2 ) 0.998 and a slope of 1.008. (b) Residual errors representing the differences between the predicted mass shift values and their values suggested by the regression model.

It would be desirable to further support the general validity of this approach by other, independent strategies. Direct measurements of the precursor labeling in the tRNA pool are too cumbersome.2 Unfortunately a more suitable standard to determine fractional synthesis is not established. However, we determined the mass shifts for free alanine and glutamate in the liver extract based on GC/MS measurements. They should match the mass shift derived from the regression. With 1.15 (estimated) versus 1.2 (GC/MS measured) for alanine and 1.09 (estimated) versus 0.98 (GC/MS measured) for glutamate, they are indeed very close. This congruence appears to serve as methodologically independent support for our regression approach. Table 1 shows the mean FSR values for individual proteins, their standard error of mean (SEM), and the number of peptides used for calculation. For 12 proteins no errors are given, because only 1 or 2 peptides could be used, whereas for the remaining proteins, 5 peptides were used on average for FSR calculation. The majority of proteins listed in Table 1 exhibited peptide mass shifts large enough to be ascribed to 13C-labeling. The FSR values for their individual peptides were calculated from eqs 5 and 6 using the precursor labeling values shown in Figure 4. Considering the peptides of all proteins of Table 1, the standard deviation of the FSR determination for an individual peptide is calculated as 1.43%/8 h. Because a FSR determination is derived on average from five peptides, the standard error of such a determination yields σFSR ) 0.65%/8h . Following the conventional definition of the limit of detection (3σFSR, LOD) and limit of quantification (10σFSR, LOQ) this translates to the threshold values of 2.0 and 6.5%/8 h. As well as the 6 MUP proteins with FSR values of >85%/8 h, 10 other proteins have FSR values above LOQ, 17 proteins have FSR values between the LOQ and LOD level, and 11 proteins are below the LOD value. For the serum albumin precursor protein (spot 35) we observed a fractional synthesis rate of 9.9%/8 h. The corresponding value for a rat labeled by oral tracer administration, after conversion to an 8-h labeling period, is expected to be 15%/8 h.9 However, our FSR values were obtained after an overnight fast and during a glucose infusion devoid of any amino acids. Under these circumstances the total hepatic protein synthesis rate should

be reduced to 60-70% of the normal fed values,25 which is in agreement with our result. The peptide mass fingerprint result of the two spots shown in Figure 2b identified the 53 kDa selenium binding protein 2. The results are based on high amino acid sequence coverage: 66% for spot 43 and 60% for spot 44. The protein turnover rates of the two spots differed significantly (p < 0.05, Mann-Whitney rank test) with FSR ) 2.4 ( 0.5%/8 h for spot 43 and FSR ) 0.1 ( 0.6%/8 h for spot 44 (see Table 1). Differing turnover rates for the same proteins associated with different 2D-PAGE positions are probably due to posttranslational modifications or isoformspecific processing rates. The large number of peptides (17) available for the latter two protein spots allows the establishment of significance despite the low difference in FSR of 2.0%/8 h. The two spots have seven peptides in common, which allows a more sensitive paired comparison that also gives a significant result at p < 0.05 (Wilcoxon match paired, signed rank test), despite the lower number of data points. When analyzing protein spots obtained for different conditions in a labeling experiment, peptide pairs can be directly compared to detect the mass shift values. The sensitivity of paired tests should be sufficient to assess changes in the synthesis of various proteins under pathological conditions. For example, the total synthesis rate in the liver increases by 20% of the basal value under sepsis.26 Only a fraction of proteins contributes to this increase so that the proteins involved should show an increase of at least 30% of basal value. Extrapolating our result from selenium binding protein we should be able to detect such an increase even for proteins with low basal FSR values at the LOQ of 6.5%/8 h. The labeling phase in this protocol lasted 8 h. Our primary objective is to demonstrate the absolute limits of fractional synthesis that can be analyzed by the fidelity of the labeling and measuring system, rather than to actually determine the turnover of the proteins concerned. Hence, we give fractional synthesis in units of %/8 h. These values can be converted with eq 8 to (%/h) or (%/d) units. However, this conversion is only valid under the (25) Balage, M.; Sinaud, S.; Prod’homme, M.; Dardevet, D.; Vary, T. C.; Kimball, S. R.; Jefferson, L. S.; Grizard, J. Am. J. Physiol. Endocrinol. Metab. 2001, 281, E565-E574. (26) Moldawer, L. L.; O’Keefe, S. J.; Bothe, A. Jr.; Bistrian, B. R.; Blackburn, G. L. Metabolism 1980, 29, 173-180.

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approximating assumption of stationary conditions for a protein, such that the actual breakdown equals the synthesis rate and that first-order kinetics applies for the breakdown. According to eq 9 FSR values can be converted to half-lives. Proteins showing FSR values of 15, 10, and 5%/8 h under our conditions then have halflives of 1, 2, and 4 days. If the labeling phase were extended to 24 h, then the described experimental procedure would permit reliable detection and quantification of proteins with half-lives of 8 days. This should account for the majority of liver proteins. Starting from 20 mg of liver tissue the fractional synthesis rates of more than 40 protein spots were quantified using a simple tracer infusion regime. We are not aware of any other approach with similar performance. The approach of Papageorgopoulos et al.9,10 is to our knowledge the most comparable. Under optimal conditions it provides both the precursor labeling and the fractional synthesis rate. It relies on the incorporation of labeled amino acids on two sequence positions of a protein and the mass spectrometric measurement of simultaneously occurring single- and doublelabeled protein fragments. However, for general proteome analysis, amino acid precursor labeling should be maintained below 20% of the total pool to avoid potential metabolic side effects of high amino acid levels on individual protein synthesis rates. Hence, for a FSR of 10%/8 h of the protein in question the likelihood to find a double label in the peptide is 0.2 × 0.2 × 0.1 ) 0.004 or 0.4%. The error in the determination of this low effect propagates in the parallel determination of FSR and precursor labeling and thereby sets the lower determination limit for this approach at FSR values of 5-10%/8 h. It also requires highprecision sector field mass spectrometry, optimized for mass distribution analysis of a specific peptide. However, it does not provide a determination limit lower than that available with our automated high-throughput MALDI-TOF measurements from 2Dgel spots.

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In this study we calculated FSR values relative to those of MUP proteins. A male mouse produces MUP proteins with a rate that accounts for more than 10% of the total hepatic protein synthesis, and synthesised protein is rapidly secreted. Accordingly, the fractional synthesis rates of the MUPs can reliably be estimated a priori as a value close to 100%/8 h, which then can be used as a normalization value for all the other FSR values derived from peptide distributions. Other tissues may fail to produce proteins with such high turnover rates. In such cases the tissue free amino acids may provide surrogate precursor labeling values. This is supported by the findings we obtained for free alanine and glutamate in the liver extract based on GC/MS measurements, but the general applicability of this approach remains to be established. Even if there is no information available about the precursor labeling, our method can be applied to establish baseline fractional synthesis values of stable isotope incorporation into proteins and measure changes thereof after some treatment to resolve the treatment effects on the fractional synthesis rates of the proteins. CONCLUSION The described technique to conveniently quantify the relative synthesis rates, or changes thereof, in a plurality of proteins adds an exciting tool to the repertoire of the proteome analyst. This is likely to be of great value in system modeling of complex biological systems.

Received for review August 27, 2004. Accepted January 7, 2005. AC048722M