Analytical Biochemistry 420 (2012) 73–83

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Analytical Biochemistry

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Measurement of human plasma proteome dynamics with 2H2O and liquidchromatography tandem mass spectrometry

John C. Price a,⇑, William E. Holmes a, Kelvin W. Li a, Nicholas A. Floreani a, Richard A. Neese a,Scott M. Turner a, Marc K. Hellerstein b,⇑a KineMed, Emeryville, CA 94608, USAb Department of Nutritional Science and Toxicology, University of California, Berkeley, CA 94702, USA

a r t i c l e i n f o

Article history:Received 23 July 2011Accepted 6 September 2011Available online 14 September 2011

Keywords:Mass spectrometryProtein turnoverDeuteriumIn vivo

0003-2697/$ - see front matter � 2011 Elsevier Inc. Adoi:10.1016/j.ab.2011.09.007

⇑ Corresponding authors.E-mail addresses: jprice@kinemed.com, drjohncp

march@berkeley.edu (M.K. Hellerstein).

a b s t r a c t

Dysfunction of protein turnover is a feature of many human diseases, and proteins are substrates inimportant biological processes. Currently, no method exists for the measurement of global protein turn-over (i.e., proteome dynamics) that can be applied in humans. Here we describe the use of metaboliclabeling with deuterium (2H) from 2H2O and liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis of mass isotopomer patterns to measure protein turnover. We show that the positionsavailable for 2H label incorporation in vivo can be calculated using peptide sequence. The isotopic incor-poration values calculated by combinatorial analysis of mass isotopomer patterns in peptides correlatevery closely with values established for individual amino acids. Inpatient and outpatient heavy waterlabeling protocols resulted in 2H label incorporation sufficient for reproducible quantitation in humans.Replacement rates were similar for peptides deriving from the same protein. Using a kinetic model toaccount for the time course of each individual’s 2H2O enrichment curves, dynamics of approximately100 proteins with half-lives ranging from 0.4 to 40 days were measured using 8 ll of plasma. The mea-sured rates were consistent with literature values. This method can be used to measure in vivo proteomehomeostasis in humans in disease and during therapeutic interventions.

� 2011 Elsevier Inc. All rights reserved.

In vivo protein concentration is controlled by the dynamic bal-ance between protein synthesis and degradation [1–3]. Coordi-nated turnover of specific proteins is a critical facet of manymetabolic and regulatory processes [4,5], and proteins serve assubstrates in a number of complex processes that are critical inhealth and disease [6]. Dynamic metabolism of proteins after syn-thesis includes proteolysis through autophagosome–lysosome andubiquitin–proteosome systems; partial proteolytic cascades as oc-cur in coagulation, complement, kallikreins, caspases, and numer-ous signaling pathways; internalization and processing ofmembrane proteins; group turnover of proteins in organelles andaggregates; and, of course, posttranslational modifications suchas phosphorylation, acetylation, and glycosylation. The use of sur-rogate measurements, such as mRNA profiling, to define changes inprotein concentration and protein turnover is likely to be con-founded by posttranscriptional regulation [7]. For example, turn-over of mitochondrial proteins is coordinated at the level of theprotein complex regardless of whether the gene was encoded onthe mouse or the mitochondrial genome [2]. This turnover is

ll rights reserved.

rice@gmail.com (J.C. Price),

regulated at the level of the tissue, the organelle, and the proteincomplex. Within this complex regulatory environment, lifetimesfor individual proteins range from minutes to years [2,8,9]. Despitebeing a fundamental component of cellular and organismalhomeostasis, generally applicable methods for measuring thedynamics of the proteome in humans and living animals have beenlacking.

Many debilitating diseases can be classified by the dysfunctionin turnover of specific proteins [10–12]; for example, Ab aggrega-tion and turnover is almost universally associated with Alzheimer’sdisease [13]. Treatment of these diseases may be most effective ifprotein turnover can be monitored and adjusted. For example, in-creased turnover of huntingtin protein reduced disease-associatedneuropathology, motor, and cognitive signs in models of Hunting-ton’s disease [14]. Although turnover of bulk tissue or selected pro-teins has been measured [15–19], measurement of dynamics on aproteome-wide scale is necessary to truly understand thesediseases.

A stable isotope method for measuring protein dynamics hasseveral fundamental advantages over simple quantification ofprotein concentrations. First, measurement of dynamics is a moresensitive measure of regulatory state such as transcription factoravailability or proteosome activity [3,20,21]. Second, recovery

Fractionate and digest proteins

LC/MS/MSbioinformatic analysis

of isotopomer distribution

proteinsynthesis

isotopically enrich drinking water

amino acid metabolism

sample tissuesover time

Pro-Glu....Lys (6-30mer)

M/Z

Inte

nsity

O

CD3

H2N

DOH

Fig.1. Experimental protocol. Isotopically enriched drinking water was provided to subjects in the study. Endogenous metabolism incorporates isotope from body water intoamino acids and proteins. Samples were collected at designated time points. Proteins from these samples were separated into different fractions using affinitychromatography and then digested for analysis by LC–MS/MS. Subsequent bioinformatic analysis of the peptide isotopomer shift was used to calculate kinetics.

74 Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83

efficiency biases many measurements of protein concentration,whereas the ratio of labeled/unlabeled species (isotopic ratios)does not change with fractionation or require complete recoveryduring processing. Moreover, integrated proteome kinetics is, inprinciple, measurable because metabolic labels can enter everynewly synthesized protein and metabolic labeling techniquesare fully translatable from model systems to humans[15,17,20,22–25].

Recently, organism-wide isotopic labeling was used to mea-sure protein turnover on thousands of individual proteins in mice[2]. In that study, greater than 99.5% isotopic enrichment of nitro-gen (15N) in dietary amino acids allowed independent measure-ment of protein turnover and precursor enrichment. The studywas an effective proof of principle that metabolic labeling and li-quid chromatography tandem mass spectrometry (LC–MS/MS)1

can be used to measure proteome dynamics. Application of thismethodology to humans is problematic, however, because it re-quires labeling of 100% of the dietary protein. Here we describe amethod for measuring in vivo protein turnover in humans usingheavy water and LC–MS/MS analysis of mass isotopomer patternsin tryptic peptides (2H2O) (Fig. 1). We also show that wide-scalestudies of protein turnover can be accomplished using far lowerisotopic enrichments (1–3%) than those used in the aforementioned15N study. Using this methodology, we present a proof-of-principlestudy measuring turnover of approximately 100 proteins in five hu-man subjects from 8 ll of unfractionated plasma. We report thatlabel incorporation is sufficient for reproducible measurement ofturnover rates, that calculation of the number of nonlabile C–Hpositions available for 2H label incorporation in vivo during proteinbiosynthesis can be calculated for any tryptic peptide, that eachindividual’s body water 2H2O enrichment curves can be accountedfor with a simple kinetic model, and that protein turnover ratesmeasured for these subjects correlate well with accepted literaturevalues for the individual proteins. We conclude that wide-scaleprotein turnover measurements can be performed using LC–MS/MS and 2H2O labeling in humans.

1 Abbreviations used: LC–MS/MS, liquid chromatography tandem mass spectrom-etry; GCRC, General Clinical Research Center; EDTA, ethylenediaminetetraacetic acid;MPE, molar percentage excess; SIM, selected ion monitoring; Q-TOF, quadrupoletime-of-flight; H/D, hydrogen/deuterium; p, precursor pool enrichment; n, number ofsites within peptide capable of incorporating label; f, fractional synthesis; k, turnoverrate constant; nAA, number of sites within amino acid capable of incorporating label;M0, monoisotopic mass; |EM0|, absolute value of change in M0 intensity; RMSE, rootmean square error; D0, time point at day 0; MIDA, mass isotopomer distributionanalysis; IgG, immunoglobulin G; SEM, standard error.

Materials and methods

In vivo labeling

All procedures and methods were approved by the University ofCalifornia, San Francisco, Committee on Human Research and bythe University of California, Berkeley, Committee for the Protectionof Human Subjects. Clinical work was performed at the GeneralClinical Research Center (GCRC) of the San Francisco General Hos-pital. Written informed consent was given for all procedures.

Two heavy water (2H2O) labeling strategies were used in thisstudy: a short-term (7-day), highly controlled inpatient methodand a longer term (6-week) outpatient method. For the inpatientgroup (subjects 4, 7, and 8), subjects stayed in the GCRC duringthe course of the study. Subjects were admitted into the GCRC atnoon. Within 15 min of admission, oral dosing of 2H2O was initi-ated. The 2H2O dosing was conducted in two phases: a ramp phaseand a maintenance phase (Fig. 2). During the ramp phase, subjectsreceived eight 50-ml doses of 70% 2H2O (Isotec–Sigma, Dayton, OH,USA) over 36 h. During the maintenance phase, subjects receivedtwo doses of either 40- or 50-ml doses every 24 h for the remain-der of the labeling period.

Subjects in the outpatient study (subjects 77 and 78) were givena supply of 50-ml doses of 70% 2H2O and instructed to self-admin-ister three doses per day during the first 7 days (ramp phase) andtwo doses per day for the duration of the experiment (maintenancephase). Compliance with the experimental protocol was monitoredby the clinical staff through intermittent collection of saliva or ur-ine samples.

Sample preparation

At each time point, blood (�10 ml) was collected in a Vacutain-er (BD Biosciences, Bedford, MA, USA) containing spray-dried eth-ylenediaminetetraacetic acid (EDTA). The cells were separatedfrom the EDTA-treated blood plasma via centrifugation (10 minat 1500g). Blood plasma was collected and stored frozen at�20 �C until use. For this study, 500 lg of plasma protein (�8 llof plasma) was used from each subject. In preparation for LC–MS/MS, the most abundant proteins were removed from plasmasamples of subjects 4, 7, and 8 using a multiaffinity spin cartridge(Hu14, Agilent, Santa Clara, CA, USA) according to the manufac-turer’s recommendations. For samples from subjects 77 and 78,albumin and other highly abundant proteins were removed usingan Affigel spin column (Bio-Rad, Hercules, CA, USA). The remaining

P E P T I D EP E P T I D E

TI

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0

0.2

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1

0 2 4 6

2 H2O

enr

ichm

ent (

MPE

)

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B C

No 2H enrichment 2H enriched

Ramp Maintenance

Mass-to-charge (m/z)

M0 M1 M2 M3D

419 419.4 419.8 420.2 420.6

D

TI

EPD

P

Fig.2. Body water enrichment determines the degree of 2H labeling on newly synthesized peptides. Body water enrichment is measured independently at each time point (A).The number of sites that can retain label (ovals) in the peptide is the sum of the sites for each amino acid (B, C). The body water enrichment at the time of synthesis, day 0 (B),through day 6 (C) defines the percentage of these sites that will bear either 1H (blue) or 2H (red). (D) Overlay of mass spectra for peptide HLSLLTTLSNR from vitamin D bindingprotein. The intensities of isotopic masses (M0, M1, M2, and M3) of the unenriched spectrum (blue) change very predictably relative to the intensities of the labeled spectrum(red) over the course of the experiment.

Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83 75

protein components of the plasma were denatured using urea (6 Murea and 100 mM Tris, pH 8.0). The solution was reduced withTCEP [tris-(2-carboxyethyl)-phosphine] (5 mM) and heated(1 min at 95 �C) to ensure that disulfide bonds within the proteinswere broken. The solution was then incubated with iodoacetamide(10 mM) in the dark for 20 min to chemically modify the reducedcysteines. The chemically modified plasma was then digested withtrypsin (Promega, Madison, WI, USA) at 37 �C overnight. Peptideswere concentrated and desalted prior to LC–MS/MS using a C18spec tip (Varian, Palo Alto, CA, USA).

Measurement of 2H enrichment in body water

Heavy water (2H2O) was administered orally throughout thecourse of each study. Total volume and body water turnover are di-rectly measurable from time-dependent changes in 2H2O enrich-ment [26]. Isotopic enrichments were measured in triplicate forall time points. Aliquots of plasma were diluted 1:100 and placedinto the caps of inverted sealed screw-capped vials for overnightdistillation at 80 �C. Body water 2H2O enrichments were

determined by direct measurement of deuterium molar percentageexcess (MPE) in water distilled from the blood plasma. MPE wasmeasured against a 2H2O standard curve using a laser waterisotope analyzer (Los Gatos Research [LGR], Los Gatos, CA, USA)according to the published method [27].

Measurement of amino acid enrichments by GC–MS

Protein components of blood plasma were precipitated from a200-ll aliquot by dilution into cold acetone (800 ll) followed byincubation at �20 �C for 1 h. Free amino acids were isolated fromthe organic supernatant after evaporation of the solvent under re-duced pressure. Dried amino acids were resuspended in 1 ml of50% acetonitrile and 50 mM K2HPO4 (pH 11.0). Pentafluorobenzylbromide (20 ll) was added, and the sample was sealed and incu-bated at 100 �C for 1 h. After cooling to room temperature, ethylacetate (600 ll) was added to each sample, followed by mixing.The top layer was then transferred to a fresh tube containingNa2SO4. The anhydrous organic solution was injected directly ontoa DB-17ms column (30 m � 0.25 mm i.d. � 0.25 lm film thickness,

76 Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83

J&W Scientific, Santa Clara, CA, USA). Analysis was performed on anAgilent 6890N gas chromatography (GC) device using CI sourcemaintained at 280 �C. The oven temperature was cycled from 140to 280 �C over a 7.5-min run. Data were collected in selected ionmonitoring (SIM) mode with a 15-s dwell time using the ions listedin Table S1 of the supplementary material.

LC–MS data acquisition and isotopomer extraction

The isotopic distributions of peptides were measured using anAgilent 6520 Q-TOF (quadrupole time-of-flight) device with chipnano source (Agilent). Each sample was injected two times peranalysis. During the first injection, MS/MS fragmentation spectrawere collected for peptide identification. During the second injec-tion, no MS/MS fragmentations were performed and a longer dwelltime (1 spectrum/s) was used in the full scan acquisition. The long-er dwell time increased the signal-to-noise ratio for the observedisotopomer patterns. MS/MS fragmentation data were analyzedusing the Agilent software package Spectrum Mill, and proteinidentifications were based on the UniProt/SwissProt database (Au-gust 2010) where species = human, trypsin digest, and carbami-domethylation of cysteine were used as restrictions on thesearch. Isotopomer patterns were extracted from the MS scan datausing the MassHunter software package (Agilent). The peptide listwith calculated neutral mass, elemental formula, and retentiontime was used to filter the observed isotope clusters. A visual basicapplication was constructed to calculate peptide elemental compo-sition from lists of peptide sequences and calculate isotopomerpatterns over a range of precursor body 2H2O enrichments (p) forthe number (n) of C–H positions actively incorporating hydrogen/deuterium (H/D) from body water (see below). Subsequent datahandling was performed using Microsoft Excel.

Calculation of turnover rate

Fractional synthesis (f) is the proportion of newly synthesizedproteins in a population, expressed as a fraction of the total pool[15,28]. Although MS can quantify a shift to higher masses in apeptide with 2H labeling, kinetic interpretation of the replacementrate of preexisting protein molecules by newly synthesized onesrequires understanding of the mass isotope pattern of newly syn-thesized species as compared with unlabeled species. The massisotopomer pattern of proteins synthesized in the presence of astable isotopically perturbed precursor pool can be calculated bycombinatorial analysis [25]. Each protein (and by extension eachtryptic peptide) acquires isotopic enrichment over time in a man-ner determined by the rate of protein turnover (k), the time-vary-ing 2H isotopic enrichment in the body water (p), and the numberof sites in the peptide capable of incorporating H/D from water (n),so both p and n must be known to calculate k [25].

Calculation of n

Tryptic peptides exhibit a value of n that is the sum of the indi-vidual values of the amino acids that make up the peptide (nAA)(Fig. 2B and Table S1). We determined values of nAA for each aminoacid in two ways. First, literature-derived estimates of n were cal-culated based on the work of Commerford and coworkers [29] inwhich the incorporation of protons from tritiated water into pro-tein-bound amino acids was quantified in long-term labeled mice.We also compared the mass isotopomer pattern in labeled pep-tides with theoretical values for n and established the best fit valuein comparison with Commerford and coworkers’ predicted valuesof n.

New proteins are synthesized from an intracellular pool of ami-no acids that are labeled in proportion to the prevailing

enrichment of tissue water (p). Due to the rapid equilibration ofwater in the body, p was measured directly in the blood plasmaat each time point over the course of each study. Because bodywater enrichment changed with time as the subject ingested theheavy water, the 2H enrichment of the amino acids that make upthe precursor pool for new proteins also changed with time in apredictable manner.

It should be noted that, at the isotopic enrichments of p used inthis study (1–3%), newly synthesized (labeled) and preexisting(unlabeled) peptide populations have m/z envelopes that overlapwith each other. In a mixed pool (i.e., for fractional syntheses be-tween 0 and 100%), deconvoluting the two subpopulations is car-ried out by the approach described previously for biochemicalpolymers [25] based on quantitative changes in abundances ofmass isotopomers (Figs. 2 and 3). Each mass isotopomer was nor-malized according to the total intensity of the isotopomer enve-lope, typically 4 masses (M0–M3) (Fig. 2D). Peptides with a massgreater than approximately 2400 Da exhibit a larger isotopomerenvelope, so 5 masses (M0–M4) were used. In this study, we basedour calculations of f on the absolute value for change in intensity ofthe normalized monoisotopic peak (|EM0|). In principle, the shift inintensity of any isotopic peak in the envelope should reveal thesame f. In practice, we find that the signal-to-noise ratio is mostfavorable for |EM0| because of the larger change in fractional abun-dance for this isotopomer (EM0 decreases while labeled speciesdistribute among EM1–EM4). The absolute EM0 value in 100%new peptides synthesized at any moment in time from the ambi-ent value of cellular 2H2O (|EM0max|) is a function of p and n, result-ing in an |EM0max| that changes continuously over time duringheavy water intake protocols. To account for this time-varying pre-cursor enrichment [p(t)], a simple kinetic model was constructedusing the SAAM program to calculate the time-dependant changesin isotopic enrichment of newly synthesized tryptic peptides(Fig. 2A) and, from this, to calculate cumulative fractional synthesis(f) and a rate constant for protein turnover (k).

In our kinetic model, the 2H2O in body water is in fast equilib-rium with the amino acid pool (p) and can be modeled kineticallyas a single pool (SAAM software) (Fig. 4). The model used the dos-ing regimen of 2H2O to fit the measured body water 2H enrich-ments over time to a body water volume and body waterturnover curve. This continuous body water enrichment tracewas then used to define the kinetically relevant p necessary to cal-culate the |EM0max| for each peptide at every point during the timecourse. The second pool is synthesized proteins and is modeledusing the measured |EM0|. The model used time-varying, pep-tide-specific |EM0max| and the measured isotopic enrichment ofeach peptide at each sampled time point to calculate a best fit rateconstant (k) for the data. The minimal kinetic model for determin-ing turnover rate constants for body water and fractional synthesisof peptides was implemented using SAAM II modeling software(University of Washington, Seattle, WA, USA). We consistently ob-served a non-zero |EM0| at time zero, and this may be due to ouruse of the theoretical natural abundance as the background spec-trum. Using the theoretical spectrum instead of an experimentalspectrum does not correct for instrumental bias. To correct forthe bias, we introduced a small fixed offset of 0.0035 (�2.5% ofthe typical |EM0|) into the model. This offset was the same for allpeptides.

Criteria for peptides used in calculation of protein turnover rates

Peptides that met our criteria for inclusion had a signal inten-sity greater than 60,000 counts, had a root mean square error(RMSE) compared with the theoretical natural abundance spectraof less than 1.5% for the day 0 (D0) sample, and were observed inmore than 80% of the time points selected for the individual

10%

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% M0 % M1 % M2

5%

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35%

% M0 % M1 % M2 % M3

Isot

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er a

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ance KFPSGTFEQVSQLVK

Theor. Natural Abundance Exp. 2.7% MPE

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% M0 % M1 % M2 % M3

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01234567

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0

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3

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SE (%

)

80 90 100 110 120Relative to literature n (%)

HLSLLTTLSNRB C

Fig.3. Comparison of measured isotopomer patterns (solid) versus theoretical predictions (open) for three peptides (A–C) from subject 78. The theoretical patterns werecalculated using the literature values for n. These peptides were selected from the instrumental signal intensity range where reproducibly accurate measures of naturalisotopomer patterns were made (D). Measurement of enrichments for free amino acids in the plasma for subject 77 (open squares) and subject 78 (closed squares) agreedwell with EM1 values computed from the known isotopic enrichment and the literature n (E). Deviations between measured spectra and a family of theoretical spectra wheren varied from 80 to 120% of the literature value (F), for peptides HLSLLTTLSNR (circles), KFPSGTFEQVSQLVK (squares), and YTFELSR (triangles), showed a minimum fordeviation from measured RMSE between 90 and 100% of the literature n.

A

BodyWater

AminoAcids Protein

Diet

Waste

B

0

0.004

0.008

0.02

0.04

0.06

0.08

0 2 4 6

measured 2H2O p

|EM0|Protein Enrichment

|EM0|

2 H2O

(MPE

)

Time (days)

Fast

Fast

Fig.4. The rates of body water enrichment and protein turnover were obtained byfitting the data according to a simple kinetic model (A) where the body water andamino acid pool are in fast equilibrium compared with the rate of body waterenrichment and protein turnover. The median enrichment values for apolipoproteinC3 from subject 4, calculated from three of the five observed peptides, are shownwith this model (B). The rate of the amino acid enrichment was derived by fittingthe body water enrichment measurements (open triangles, 0.197 day�1). The modelapplies this precursor enrichment rate to every protein associated with thissubject’s study. Protein turnover (dashed line) was measured by fitting the medianenrichment for the family of peptides associated with the protein (solid diamonds).The error bars represent the standard deviation among peptide enrichments.

Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83 77

subject. Peptides were screened according to the precision re-ported by SAAM, calculated as a percentage coefficient of variationas reported previously [30]. Only peptides for which the rate con-stant could be defined with less than 30% coefficient of variationwere then used in calculating the protein turnover rate. Proteinturnover rates were calculated as the median of the peptide popu-lation that passed these criteria for each protein.

Results

Body water labeling

Two standardized body water enrichment protocols were em-ployed in this study: a short-term inpatient strategy and a long-term outpatient strategy. The goal in both methods was to bringbody water 2H enrichments to a sufficient level that would bemaintained for the duration of the experiment (Fig. 2). In theshort-term experiment, doses of 2H2O were administered as a load-ing dose in an inpatient metabolic ward setting in order to rapidlyattain near-plateau enrichment. In spite of the careful control overdosing, the slope of the enrichment ramp and the stability of theplateau varied (see Fig. S1 in supplementary material). Subject 4had a faster body water labeling rate (19.8% day�1) and attaineda higher total enrichment (1 MPE) than subject 7 (13.5% day�1

and 0.74 MPE) and subject 8 (11.8% day�1 and 0.64 MPE). Theenrichment ramp for long-term labeled subjects (77 and 78) lastedsignificantly longer, achieving both higher total enrichment andslower observed body water turnover (1.5 MPE and 6% day�1 forsubject 77 and 2.7 MPE and 3% day�1 for subject 78). The bodywater curve for subject 77 attained near-plateau enrichment with-in the first 10 days and maintained that plateau enrichment for theduration of the experiment. For subject 78, the increase in bodywater enrichment continued slowly throughout the 41 days ofthe experiment at a rate of 3% day�1 (Fig. S1).

Validation of n in humans

We began by comparing values for the number of stably labeledC–H sites from body 2H2O in each amino acid from literature mea-surements of Commerford and coworkers from tritium labeling inmice [29] with values calculated by mass isotopomer distributionanalysis (MIDA) [25] in peptides. Amino acid values of n are ex-pressed as a ratio to the previously determined n = 4 [15,17] foralanine as our point of reference (Table S1).

Experimental confirmation of Commerford and coworkers’ liter-ature values in humans was performed in two different ways. First,we tested and optimized the accuracy of our mass isotopomerabundance measurement on unlabeled peptides against the

78 Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83

theoretical isotopomer patterns based on natural isotope abun-dances (Figs. 3A–C and Fig. S2). For these comparisons, we usedthe RMSE as a general measurement of accuracy. An RMSE of lessthan 1.5% was considered as acceptable, with an RMSE below 1%being optimal. Comparison of multiple natural abundance peptideisotopomer patterns showed that signal intensity strongly influ-ences accuracy (Fig. 3D). Because of the correlation with signalintensity, we established a standard minimum total signal inten-sity of 60,000 counts in order for peptides to be considered inthe analysis. We also found that the RMSE value of the unlabeledpeptide was a useful validation of the MS/MS identification, allow-ing us to sort out misidentified sequences from our data set. Next,we compared measured spectra against theoretical predictions forisotopomer patterns from proteins expected to be fully labeled(Figs. 3A–C and Fig. S2). For this, we targeted peptides in long-termsubjects 77 and 78 from proteins with known fast turnover ratesthat should be 100% replaced during the labeling period. We eval-uated deviations between measured mass isotopomer patterns forthese peptides and a family of theoretical spectra where n variedaround Commerford and coworkers’ value. We found that for mostpeptides, the best fit n matched literature values closely. Individualpeptides showed better agreement with a slightly adjusted n(Fig. 3F and Fig. S2). Some random variation was observed, but arange of minimum RMSE values (e.g., subject 77) extended from90 to 100% of the expected value for n.

As a second method to validate the literature n values, we mea-sured enrichments of individual free amino acids in blood collectedat the final time point of subjects 77 and 78 (after >40 days of 2H2Oexposure). We measured the enrichments for 9 to 12 amino acidsin these samples (Table S1). There was a strong linear correlationbetween measured enrichments and the theoretical enrichmentsexpected for the literature n values at the measured p. We havemade similar observations of close correlation with Commerfordand coworkers’ values of n in mice based on both mass isotopomerfits in peptides from 100% turned over proteins and enrichments offree amino acids (J.C. Price et al., manuscript in preparation). Thesefindings support the use of Commerford and coworkers’ n values incalculation of maximal label incorporation (Fig. 3E).

Modeling time-dependent precursor enrichment (p)

A labeling scenario in which fixed body water 2H2O enrichmentis instantaneously achieved and maintained for the duration of theexperiment would be straightforward to interpret. In this scenario,a simple exponential rise in plateau fits for peptides will give theturnover rate constant (k) values. Because rapid changes in bodywater 2H2O enrichments have the risk of vertigo in some people[31], immediate attainment of plateau 2H2O enrichments is notfeasible in human subjects. It is possible, however, to shorten theramp time to 24 to 48 h as we designed for subjects 4, 7, and 8.For these subjects, an approximate steady state was achieved fromday 2 to day 6. Very slow turnover proteins (half-lives of weeks)will not, however, incorporate sufficient label during the time scaleof several days to give rate information by this approach. Extend-ing the precursor pool plateau over weeks will allow slow turnoverproteins to be characterized, but proteins with fast turnover (half-lives of hours to a few days) will be fully labeled and not reveal ki-netic information if time points sampled begin after a week or so.Accordingly, the classic plateau precursor pool approach is subjectto certain limitations for kinetic analysis when the products exhibita wide range of half-lives, as is the case for proteome dynamics.

Interestingly, imposition of a nonplateau, continuously risingprecursor pool enrichment overcomes some of these limitationsand allows proteins of any half-life to be characterized with a sin-gle sampling protocol. For this, a model based on differential equa-tions is required:

EM0 ¼ abundance M0Pni¼0abundance Mi

� �t

� abundance M0Pni¼0abundance Mi

� �t¼0

ð1Þ

dðjEM0jÞdt

¼ kðjEM0maxj � jEM0tjÞ ð2Þ

Accordingly, we developed a simple kinetic model to determineprotein turnover rates that accounted for time-varying body waterenrichment (Fig. 4 and Eqs. (1) and (2)). Loss of M0 (EM0 in Eq. (1))is calculated as the change in the abundance of the M0 mass iso-topomer from its theoretical natural abundance, as described pre-viously [25]. First, the measured body water enrichments are fit toa body water model to estimate a continuous body water enrich-ment curve over time. For a given body water enrichment, the iso-topic enrichment in a newly synthesized peptide is calculable bythe use of combinatorial probabilities [25] based on n in the pep-tide. This generates a continuous curve for the newly synthesizedpeptide over time. For a steady-state pool that has unchanging size(V), labeled peptides are introduced into the pool at a rate ofk ⁄ V ⁄ |EM0max|, where |EM0max| represents the enrichment ofthe newly synthesized peptide entering the population at eachpoint in time. Concurrently, labeled peptides are degraded fromthe pool at a rate of k ⁄ V ⁄ |EM0|t, where |EM0|t represents theenrichment of the peptide in the population at that point in time.Accordingly, a simple mass balance of labeled peptides yields Eq.(2), where both |EM0| and |EM0max| are functions of time. Note thatalthough EM0 is mathematically a negative number, we have ex-pressed it as an absolute value for simplicity.

This model works equally well for any enrichment curve that isconstrained by our experimental data. The five subjects had arange of body water enrichment rates that spanned from 5 to20% day�1. Four of the subjects had a typical curve of isotope incor-poration where the ramp in body water enrichment reached a sta-ble plateau, and subject 78 exhibited enrichment that continued toramp over the study period (Fig. S1). Regardless of these individualvariations, kinetics of the plasma proteome could be measured bythe model. Indeed, a rise in body water (p) enrichment allowed themodel to define with high confidence the rate constants for fasterturnover proteins even during later time samples. For example, insubject 78, who exhibited steadily rising body water enrichmentsover time, proteins such as alpha-1-acid glycoprotein 1 (half lifeof 3 days) and alpha-1-antitrypsin (half life of 2 days) would beat near maximal label values by the first time point (day 6), andwould show little kinetic information in subsequent time points,under a standard early plateau labeling regimen. In contrast, withcontinually increasing precursor pool enrichment, each time pointin this subject contributed information to the fit even at 26 and41 days.

Plasma proteome dynamics

Using MS/MS fragmentation, we identified a total of 2630 un-ique peptides in plasma from the five subjects (Table S2). We usedfour selection criteria to remove low confidence information forpeptides from the analysis (see above): signal intensity (>60,000counts), RMSE for mass isotopomer abundance measurements inan unlabeled sample of less than 1.5%, presence of the peptide inmore than 80% of the time points, and a rate constant that couldbe defined with less than 30% coefficient of variation from theincorporation curve. The number and proportion of peptides thatyielded kinetic information varied between subjects. In subject 4,for example, we observed 1939 unique peptides. Of these peptides,65% (1268) had isotopomer patterns of sufficient signal intensityfor analysis. Of these abundant peptides, 44% (561) were observedin more than 80% of the time points and were carried on for furtheranalysis. For the |EM0| kinetics, 80% (444) of the 561 peptides had a

Table 1Median turnover rates for human plasma proteins.

Protein UniProtnumber

Subject k(day�1)

SEM Protein UniProtnumber

Subject k(day�1)

SEM Protein UniProtnumber

Subject k(day�1)

SEM

Afamin P43652 4 0.153 0.045 Compl. C6 P13671 4 0.362 Ig mu C P01871 7 0.1447 0.238 0.077 8 0.176 8 0.0778 0.247 0.061 Compl. C8 b P07358 4 0.153 78 0.130 0.111

a-1-glycoprot 1 P02763 77 0.098 0.000 7 0.371 0.235 Ig mu heavydisease

P04220 78 0.046

78 0.154 0.053 8 0.184 0.092 Insulin-likesubunit

P35858 4 0.388 0.100

a-1-acid glycoprot 2 P19652 77 0.144 Compl.C8 c P07360 8 0.265 7 1.023 0.00078 0.150 0.067 Compl. C9 P02748 4 0.285 0.036 8 0.455 0.016

a-1-antichymotryp P01011 4 0.190 0.143 7 0.322 0.097 Inter-a-trypinhib H1

P19827 4 0.281 0.109

7 0.174 0.059 8 0.302 0.118 7 0.432 0.3028 0.230 0.266 Compl. fact B P00751 4 0.262 0.096 8 0.515 0.277

78 0.031 7 0.411 0.229 Inter-a-trypinhib H2

P19823 4 0.209 0.052

a-1-antitryps P01009 77 0.097 0.042 8 0.347 0.088 7 0.303 0.10778 0.132 0.058 Compl. fact H P08603 4 0.134 0.039 8 0.385 0.097

a-1B-glycoprot P04217 4 0.228 0.084 7 0.169 0.050 Inter-a-trypinhib H4

Q14624 4 0.356 0.134

7 0.254 0.090 8 0.196 0.064 7 0.531 0.1648 0.250 0.118 Compl. factor I P05156 4 0.216 8 0.486 0.152

78 0.228 0.144 Csteroid-binding

P08185 4 0.104 0.012 Kallistatin P29622 4 0.264 0.053

a-2-antiplasmin P08697 4 0.158 0.042 7 0.189 7 0.538 0.1197 0.370 8 0.180 0.033 8 0.3258 0.107 77 0.614 Kininogen-1 P01042 4 0.243 0.056

a-2-HS-glycoprotein

P02765 4 0.179 0.055 78 0.252 0.225 7 0.264 0.117

7 0.265 0.042 Dynein 12,axonemal

Q6ZR08 78 0.090 8 0.282 0.117

8 0.206 0.062 Fibrillin-3 Q75N90 4 0.188 0.040 Leucine-rich a-2-glyco

P02750 4 0.280 0.099

78 0.126 0.040 Fibrinogen a P02671 4 0.187 0.094 7 0.174 0.026a-2-macroglobulin P01023 4 0.047 0.012 7 0.170 0.070 8 0.186 0.034

7 0.061 8 0.168 0.067 77 0.091 0.1298 0.102 Fibrinogen b P02675 4 0.119 0.036 78 0.188 0.051

77 0.040 0.034 7 0.128 0.031 Lumican P51884 8 0.04778 0.044 0.009 8 0.177 0.074 N-acetylmur-

L-ala amidaseQ96PD5 4 0.204

Angiotensinogen P01019 4 0.344 0.059 Fibrinogen c P02679 4 0.135 0.027 7 0.2117 0.456 7 0.145 0.059 8 0.187 0.0468 0.423 0.075 8 0.234 0.111 Obscurin Q5VST9 78 0.016

78 0.096 0.070 Fibronectin P02751 4 0.394 0.127 Pigment epith-fact

P36955 4 0.469

Antithrombin-III P01008 4 0.366 0.105 7 0.489 0.085 7 0.4627 0.396 0.133 8 0.580 0.240 8 0.4978 0.420 0.199 Ficolin-3 O75636 4 0.185 Plasma prot C1

inhibP05155 4 0.190

Apolipoprotein A-II P02652 4 0.182 0.065 Gelsolin P06396 4 0.220 0.012 7 0.2887 0.258 0.132 7 0.213 0.038 8 0.3568 0.203 0.119 8 0.258 0.023 Plasminogen P00747 4 0.337 0.181

Apolipoprotein A-IV P06727 4 0.468 0.217 Haptoglobin P00738 7 0.342 7 0.386 0.1267 0.811 0.332 78 0.339 0.110 8 0.370 0.1298 0.703 0.363 Haptoglo-rel P00739 4 0.238 Platelet basic P02775 7 0.245 0.120

Apolipoprotein C-III P02656 4 0.803 0.170 7 0.343 0.026 Polyhom 3 Q8NDX5 78 0.0057 0.778 8 0.356 AMBP P02760 4 0.222 0.002

Apolipoprotein E P02649 4 1.160 0.831 78 0.559 0.761 7 0.408 0.1338 0.706 Hem sub a P69905 78 0.017 8 0.465 0.054

ATP-dep RNAhelicase

Q14562 78 0.054 Hemopexin P02790 4 0.122 0.086 Prothrombin P00734 4 0.197 0.059

b-2-glycoprot 1 P02749 4 0.541 0.192 7 0.124 0.068 7 0.320 0.1377 0.584 0.204 8 0.107 0.058 8 0.278 0.0688 0.582 0.295 Hep. Cof. 2 P05546 4 0.209 0.026 78 0.190 0.053

b-2-microglobulin P61769 4 0.252 7 0.200 hydroxypyrisomerase

Q5T013 4 0.074

Cadherin-likeprotein 26

Q8IXH8 78 0.018 0.001 8 0.331 Ras-rel GTP-bind A

Q7L523 78 0.139

Carboxypept N 2 P22792 7 0.283 His-rich glyco P04196 4 0.299 Retinol-binding 4

P02753 4 0.967

transl initiationfactor

O43310 8 0.338 7 0.408 RRN3-like 1 A6NIE6 4 0.119

Ceruloplasmin P00450 4 0.112 0.043 8 0.387 0.052 7 0.125 0.0107 0.138 0.073 Ig a-1 C P01876 77 0.059 0.011 8 0.094

(continued on next page)

Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83 79

Table 1 (continued)

Protein UniProtnumber

Subject k(day�1)

SEM Protein UniProtnumber

Subject k(day�1)

SEM Protein UniProtnumber

Subject k(day�1)

SEM

8 0.128 0.042 78 0.062 0.012 Seleno P P49908 4 0.58677 0.044 Ig a-2 C P01877 77 0.081 Ser/thr-kinase

Chk1O14757 78 0.002

78 0.056 0.011 78 0.051 0.010Serotransferrin P02787 4 0.090 0.007Clusterin P10909 4 0.674 0.400 Ig c-1 C P01857 78 0.022 0.006 7 0.152

7 1.050 0.793 Ig c-2 C P01859 77 0.012 8 0.110 0.0208 0.982 0.639 78 0.019 0.004 77 0.086 0.013

Coagulation factor X P00742 4 0.469 Ig c-3 C P01860 78 0.017 78 0.048 0.013Coagulation factor

XIIP00748 4 0.267 0.006 Ig c-4 C P01861 77 0.012 0.000 Serum

albuminP02768 4 0.048 0.011

7 0.391 0.114 78 0.018 0.003 7 0.063 0.0618 0.199 0.026 Ig heavy V-I

EUP01742 78 0.036 0.002 8 0.048 0.010

Compl. C1q subunitA

P02745 7 0.200 0.009 Ig heavy V-IIIKOL

P01772 78 0.033 77 0.038 0.064

8 0.148 Ig heavy V-IIITEI

P01777 78 0.030 0.000 78 0.023 0.004

Compl. C1q subunitB

P02746 4 0.181 Ig heavy V-IIITRO

P01762 78 0.015 Paraox/arylesterase 1

P27169 4 0.060

Compl. C1q subunitC

P02747 4 0.132 0.035 Ig j C P01834 78 0.030 0.006 TBC1 member8B

Q0IIM8 78 0.023 0.002

7 0.117 0.066 Ig j V-II Cum P01614 78 0.027 Thyr-bindglobul

P05543 8 0.245

8 0.252 0.116 Ig j V-II TEW P01617 78 0.032 0.004 Titin Q8WZ42 4 0.180 0.020Compl. C1r sub P00736 4 0.846 0.375 Ig j V-III SIE P01620 78 0.034 8 0.195 0.036

8 0.535 0.100 Ig j V-III VG(Frag)

P04433 77 0.013 TLD domain-KIAA1609

Q6P9B6 7 0.080

Compl. C1r sub-like Q9NZP8 4 0.735 78 0.028 8 0.089Compl. C1s sub P09871 4 0.385 0.245 Ig j V-IV

(Frag)P06312 77 0.013 Transthyretin P02766 78 0.319 0.281

8 0.418 Ig j V-IV Len P01625 78 0.030 Vitamin D-binding

P02774 4 0.305 0.079

Compl. C2 P06681 8 0.380 Ig k V-III LOI P80748 78 0.020 7 0.364 0.107Compl. C3 P01024 4 0.234 0.070 Ig k-1 C s P0CG04 77 0.031 0.001 8 0.387 0.135

7 0.370 0.106 78 0.030 0.004 78 0.321 0.2518 0.279 0.088 Ig k-2 C s P0CG05 77 0.031 Vitronectin P04004 4 0.690 0.253

Compl. C4-A P0C0L4 4 0.264 0.075 78 0.027 7 0.8647 0.337 0.103 Ig k-3 C s P0CG06 4 0.362 0.172 8 0.727 0.0878 0.332 0.134 Zinc-a-2-glyco P25311 4 0.348 0.117

Compl. C5 P01031 4 0.218 0.065 7 0.379 0.0497 0.170 0.038 8 0.398 0.1098 0.434 0.151 78 0.350 0.059

Note. Criteria for inclusion of peptides in the median calculation were as follows: (i) present in at least 80% of time points; (ii) signal intensity greater than 60,000; (iii) RMSEof the day 0 or unlabeled sample below 1.5%; (iv) coefficient of variation for model fit lower than 30%. The SEM represents deviation in k between peptides assigned to thisprotein.

80 Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83

precision ratio sufficient for inclusion. These peptides were thengrouped together according to protein of origin for the calculationof protein turnover rates for this subject.

Using two different depletion strategies (Affigel and Hu14 spincolumns), we were able to quickly access different portions of theplasma proteome. Using either method, we observed approxi-mately 50 proteins. The difference between the two methodswas primarily whether immunoglobulins were present or not.Immunoglobulins are relatively high-concentration proteins inthe plasma proteome, and the literature value for the turnover ofimmunoglobulin G (IgG) is approximately 14 days. By using theAffigel depletion for preparation of serum from the long-term la-beled subjects, IgG and other immunoglobulins were retainedand the longer experimental timeframe enabled us to observekinetics. In contrast, the labeling timeframe for subjects 4, 7, and8 was not long enough to allow good measurement of IgG kinetics.However, use of the Hu14 column allowed us to remove these pro-teins and delve into a different portion of the proteome.

Peptides from the observed proteins were evaluated in thesame manner. For the five subjects, we measured kinetics on a to-tal of 334 proteins, with many of these proteins being observed inmultiple subjects, resulting in a total of 114 unique proteins

(Table 1). Deviations in observed fractional synthesis (f) betweenpeptides of the same protein were uniformly greatest at the firsttime point, where enrichment was lowest. At later time points, asisotopic enrichment increased, the standard deviation for f calcu-lated for peptides belonging to the same protein decreased. Theaverage observed standard error (SEM) for calculated k amongpeptides from a protein was 5% (Fig. S3), with fast turnover pro-teins exhibiting wider distributions among peptides. Proteinswith fast turnover also had fewer total peptides, and this metour selection criteria (Fig. S3). The turnover of albumin and multi-ple other plasma proteins was measured previously using a vari-ety of methods, allowing us to independently validate themeasured rate constants for selected proteins (Table 2. The rateconstants and half-lives calculated for these proteins were consis-tent with literature values.

Discussion

Measurement of in vivo turnover is critical to understanding bio-logical homeostasis and the perturbations due to disease [20,32].Previous studies of in vivo protein turnover have historically been

Table 2Comparison of turnover rates measured in this study with literature values.

Protein UniProt number Rate in this study (% k/day) Literature

Subject 4 Subject 7 Subject 8 Subject 77 Subject 78 Rate (%) References

Albumin P02768 4.80 6.30 4.80 3.80 2.4 4–12 [24,44–47]Transferrin P02787 8.90 15.20 11 8.60 4.8 7–20 [48–50]Fibrinogen P02671 18.70 16.90 16.80 15–34 [47,51,52]Fibrinogen P02675 11.90 12.80 17.70 15–34 [47,51,52]Fibrinogen P02679 13.50 14.50 23.40 15–34 [47,51,52]Ceruloplasmin P00450 11.20 13.80 12.80 4.40 5.7 13–16 [53,54]Fibronectin P02751 39.40 48.90 58 35–45 [55,56]

Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83 81

constrained by two central limitations. First, the isotopic content ofthe true precursor pool for protein synthesis (tRNA–amino acids inthe cell), is not easily accessible for measurement and has a variableand nonconstant relationship to measurable extracellular or intra-cellular free amino acid label content that depends on many factors,including physiological state, amino acid chosen, tissue, and species[1,3,18]. Determining the enrichment or specific activity of the trueprecursor pool for protein synthesis has thereby impeded proteinsynthesis measurements for many years [3,16,33]. Second, exten-sive sample preparation has been required to ensure analyticallypure proteins [3,15,18], and this has limited applications to thebroad proteome.

LC–MS/MS provides a potential solution to both of these prob-lems and has been applied to the measurement of protein turnoverin bacteria, cell systems, and animals [2,16,32,34,35]. The use ofMIDA, or combinatorial mathematical analysis of labeled monomerpatterns in a polymer [25], represents a robust solution for the pre-cursor pool biosynthetic problem that has been used for most clas-ses of biological polymers, including lipids, carbohydrates,glycosaminoglycans, and proteins [20]. The size and complexityof proteins make application of MIDA more difficult technically,although we [15,36,37] and others [28,33] have applied this ap-proach to determine precursor pool enrichments for individualproteins such as plasma albumin. Moreover, LC–MS/MS analysis al-lows mass isotopomer patterns in hundreds or thousands of pep-tides to be measured concurrently, providing a potentiallydefinitive solution to the measurement of proteome dynamics inliving animals, including humans.

Several issues needed to be resolved for this approach to bepractical, however, including the optimal label and labeling strat-egy, how to resolve all precursor pool label measurement issues,whether isotope enrichments can be measured reliably in the scanmode (required for large numbers of peptides), whether kinetics ofproteins over a broad range of half-lives can be measured in a sin-gle protocol, and how reliable kinetic estimates can be for dozensor hundreds of proteins at once as compared to individual proteinmeasurements. The current work was designed to address thesequestions in human subjects.

The first question asked what label can be used and what the opti-mal labeling protocols are. In a recent mouse proteome dynamicsstudy [2], all 20 amino acids enriched to 99.8% 15N were suppliedas the sole protein source, flooding the precursor pools. By enrichingrapidly to close to 100% 15N, the isotopic envelopes of newly synthe-sized peptide populations were completely separated from naturalabundance masses, allowing quantitation of labeled versus preexist-ing populations for each peptide at each time point. Although suc-cessful in mice, achieving such high enrichments in humans wouldbe difficult in a clinical setting and near impossible in an extendedoutpatient study. Without the extremely high amino acid enrich-ments, however, labeled and unlabeled mass spectra of peptidesoverlap and p is poorly defined, making calculation of f very difficult.The same considerations apply to the use of individual labeled amino

acids, such as 2H3-labeled leucine and 15N-labeled glycine, for LC–MS/MS analysis of peptides. In addition, the issue of what label toadminister is closely linked to the precursor pool problem.

In contrast, for 2H2O labeling, measurement of p is straightfor-ward (i.e., body water 2H2O enrichment) and has been used suc-cessfully for extended periods of time in large numbers ofhumans [23,26,38]. We and others have shown that 2H2O labelingcan be used effectively for the measurement of protein turnoverthrough metabolic labeling of nonlabile covalent C–H positions innonessential amino acids [15,17,28,36]. The question with 2H2Olabeling is what is n (the number of repeating monomeric labelunits in the polymer [25,39]). Here we show that n can also be reli-ably determined from 2H2O labeling. Literature values for n in indi-vidual amino acids (nAA) fit closely to experimentally measuredvalues of n in peptides, as determined using MIDA (Fig. 3). Thesevalues were also closely correlated with free amino acid enrich-ments after prolonged labeling in the subjects. The same findingshave been observed in mice (unpublished). Thus, we can estimatethe peptide n as the sum of the nAA values for component aminoacids (Fig. 2).

Within the current data set, we observe that the minimum devi-ation between experiment and theory is from 90 to 110% of the lit-erature estimates (Fig. 3F and Fig. S2). Some variation of n isexpected in vivo. It is well established that amino acid metabolismwill change to balance the dietary supply. Biosynthesis of proline,for example, is known to be regulated according to the dietaryavailability [40,41]. Thus, in the case of proline, n values could varyfrom 1 to 7 depending on daily meal composition. Commerford andcoworkers’ value for proline (2.54) seems to be a good approxima-tion under normal conditions. Comparison of theoretical enrich-ments based on n = 2.54 agreed well with proline isolated fromthe plasma of subject 78 (Fig. 3E and Table S1). Recently, Kasumovand coworkers calculated n values for four peptides from mouseplasma albumin by fitting isotopomer spectra for fully labeled pep-tides [28]. Those calculated values are uniformly lower (�85% of n)compared with Commerford and coworkers’ estimates and our re-sults. The reason for the lower values observed by Kasumov andcoworkers may be due, in part, to recycling of unlabeled aminoacids in the cell.

The next question was whether sufficient reliability of isotopeenrichments could be measured in large numbers of peptides to al-low reproducible kinetic estimates. Several investigators haveshown that in ion trap instruments such as Orbitrap, accurate iso-topomer measurements are best achieved by dramatically narrow-ing the measured m/z range to focus on a single peptide (i.e., use ofSIM mode) [28,42]. We observed, in contrast, that using a Q-TOFinstrument, accurate mass isotopomer measurements could bemade concurrently on a large number of abundant peptides overa wide m/z range (Fig. 3D). The good correlations for peptides with-in a protein and the accuracy of mass isotopomer abundances inunlabeled peptides support the precision and accuracy of the ana-lytic results.

ApoE k= 1.7 day-1

ApoC3 k= 0.6 day-1

CompC3 k= 0.23 day-1

Hemopexin k= 0.07 day-1

Albumin k= 0.04 day-1

0

0.02

0.04

0.06

0.08

0 2 4 6

Nor

mal

ized

|EM

0|

Time (days)

Fig.5. Comparison of time-dependent enrichment measured for peptides fromseveral proteins in subject 4. Rate constants are derived from the model fits (lines)of the measured enrichments (symbols).

Subject 4Subject 7Subject 8

CeruloplasminCeruloplasmin P00450P00450

α α-2HS-glycoprotein-2HS-glycoprotein P02765P02765

ProthrombinProthrombin P00734P00734

Zn-2-glycoproteinZn-2-glycoprotein P25311P25311

Apolipoprotein A-IVApolipoprotein A-IV P06727P06727

β-2-glycoprotein-2-glycoprotein 1 P02749P02749

Anti-thrombinAnti-thrombin P01008P01008

α-1B-glycoprotein-1B-glycoprotein P04217P04217

Turnover rate constant (day-1)0 0.2 0.4 0.6 0.8 1

Fig.6. Dot plot comparison of several proteins in three subjects: subject 4(triangles), subject 7 (squares), and subject 8 (circles). Proteins with faster turnoverrates tended to show the greatest variation from subject to subject.

82 Measurement of proteome dynamics / J.C. Price et al. / Anal. Biochem. 420 (2012) 73–83

Here we have described a simple, two-pool kinetic model to ac-count for changes in body water enrichment over time, as is likelyto occur in the human study setting. This model allowed turnoverto be measured on more than 100 different proteins with a widerange of rates (Fig. 6). Interpreting the enrichment curves with akinetic model to incorporate the body water enrichment into theobserved isotopic labeling rate (Fig. 2A) was important for calcula-tion of k. This model was possible because of two factors: thecapacity to measure body water enrichment (p) over time andthe ability to calculate n and thereby |EM0|max. Using the indepen-dently measured p and the summed value of n in each peptide, weare able to model the time-dependent peptide enrichments andcover a wide range of turnover rates (Fig. 5).

Finally, it was not previously clear whether reliable kineticmeasurements could be made for very large numbers of proteinsmeasured concurrently by LC–MS/MS. The protein turnover rateconstants that we measured fall into published ranges (Table 2).The availability of multiple peptides from each protein serves asinternal replicates, improving confidence in kinetic parameters.An important technical point here is that much broader coverageof the plasma proteome can be achieved in future studies by theuse of off-line sample fractionation procedures prior to or follow-ing trypsin digestion. Plasma is well known as a difficult targetfor proteomics because of the large protein concentration gradient[43]. Using two different depletion strategies, we were able toquickly access different protein populations within the plasma.We have observed that dividing a proteomic mixture into approx-imately 10 fractions (by gel electrophoresis or LC) results in a 5- to10-fold increase in the number of kinetically measurable peptides(J.C. Price et al., unpublished).

Interestingly, we find that a continual ramp in enrichment maybe the most effective strategy for measuring the widest range ofturnover rates. In subject 78, a continual ramp of enrichmentwas observed over the 41 days of the experiment. This samplingprotocol had the first time point at 6 days, where we would haveexpected proteins with k > 0.2 day�1 to be close to plateau satura-tion with label by day 6, as is observed in subject 77. In contrast,the continual ramp in subject 78 allows us to extract rate informa-tion for fast turnover proteins even at late time points. By applyinga similar ramp in body water enrichment in future experiments,we may be able to measure large ranges of turnover rates in humantissues or biological fluids such as cerebrospinal fluid.

Conclusions

We have shown, for the first time, that the synthesis rates oflarge numbers of proteins can be measured in human plasma bylabeling with heavy water and monitoring changes in peptide iso-

tope abundances by LC–MS/MS. The changes observed in isotopeenrichments of peptides observed when using well-tolerated hea-vy water labeling protocols were sufficient for reliable measure-ment of proteome kinetics. Moreover, technical problemsrelevant to classic protein synthesis measurements, includingassessment of precursor pool enrichments (p) and the number ofhydrogen atoms incorporated from body water into peptides dur-ing protein biosynthesis (n), have definitive solutions with the hea-vy water labeling method. Non-steady-state levels of body water(precursor pool) enrichments can be accounted for by simple com-puter modeling, thereby allowing routine outpatient labeling pro-tocols to be used in human subjects. This approach allowsproteome network dynamics to be studied in vivo, potentiallybringing a new dimension to our understanding of human health.

Acknowledgments

We thank Lisa Misell and Patrizia Fanara for their assistance incollection of the clinical samples. We also thank Tim Riff andChancy Fessler for their assistance in collecting the gas chromatog-raphy data. All of the authors have a financial interest in KineMed.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.ab.2011.09.007.

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